Intelligent Document Processing (IDP) is a technology that uses AI and machine learning (ML) to automate document classification, data extraction, validation, and delivery.

Optical Character Recognition (OCR) converts images or scanned documents into machine-readable text. It identifies the words and characters on a page but does not understand their meaning, context, or how one document differs from another.

Both Intelligent Document Processing (IDP) and Generative AI (mostly LLMs) leverage modern AI and machine learning, but they are designed for different purposes.

IDP is purpose-built for high-volume, production document workflows. It delivers an end-to-end process for OCR, classification, extraction, formatting, validation, and exception handling. The focus is on producing consistent, standardized, and repeatable results with confidence scores, auditability, and high-throughput performance.

Generative AI, by contrast, is general-purpose. It excels at understanding context, generating and summarizing text, answering questions, and handling open-ended tasks. However, its outputs can vary from one run to another and are typically returned as free-form text that must be structured before they can be used in business processes. LLMs are not inherently optimized for precise, field-by-field extraction at scale.

From a deployment and governance perspective, IDP platforms are commonly run on-premises or within private cloud environments, providing full data control and eliminating external calls. Generative AI solutions often rely on large hosted models and may require additional controls to meet strict data residency and compliance requirements.

In practice, most enterprise document workflows require more than an LLM alone can provide. The strongest and most effective approach combines IDP for consistent, production-grade processing and LLMs for context understanding, enrichment, and edge cases.

We can help determine when to use each and design a solution that best fits your requirements.

IDPS (Intelligent Document Processing Solutions) provides end-to-end IDP platforms that automate document classification and data extraction. Drawing on more than a decade of real-world experience, we design complete, production-ready solutions tailored to each customer’s operational needs.

We offer two primary products. IDPS Comprehend is our advanced document classification and extraction engine, delivering end-to-end processing that includes OCR, classification, extraction, formatting, validation, and related workflows. IDPS Automate builds on Comprehend by adding tracking, monitoring, logging, and reporting, creating a comprehensive, all-in-one solution for document operations.

Our design philosophy is to deliver capabilities that cover every stage of document processing while remaining modular and reusable. This allows us to configure a solution that fits your environment and take on as much or as little of the workflow as needed. For example, if you already have an existing platform but need stronger classification or extraction, we can integrate into your current process and address only the gaps. This approach enables you to preserve what is already working, avoid duplicate systems, and leverage your existing monitoring and reporting investments.

We are designed for organizations that need precision, control, and production-grade performance, not a one-size-fits-all, multi-tenant SaaS platform. Our solution can be deployed on-premises, in your private cloud, or in fully disconnected environments, so your documents never have to leave your security boundary. This gives you complete ownership of your data, infrastructure, and compliance posture.

Instead of relying on generic models built for broad use cases, we create and optimize models for your specific document types and workflows. The result is higher page-level and field-level accuracy, more reliable automation, and structured, validated output that flows directly into your downstream systems. Every result is transparent, confidence-scored, and auditable.

We also provide full control over model versioning, rules, and pipelines, allowing the platform to evolve as your documents and business processes change. Combined with flexible deployment and commercial options, this delivers a high-performance IDP solution that aligns to your operating model rather than forcing you to adapt to a vendor’s platform.

In most cases, your data does not need to leave your security boundary and remains within your environment. This allows us to align with your existing data protection controls and compliance requirements without additional recertification or revalidation. Data would only leave your environment at your request, for example when using third-party services such as OCR or LLMs. On-premises options are available for these capabilities.

We recommend secure deployment practices, including strong encryption in transit and at rest, strict access controls, environment isolation, and other industry-standard safeguards. The exact implementation will depend on your infrastructure, compliance obligations, and operational requirements.

We can discuss your specific security and data protection needs in more detail during a discovery call.

Yes. The platform can be deployed on-premises, within your private cloud, as a SaaS offering, or in a hybrid architecture, based on your security, compliance, and operational requirements.

In most cases, the solution is delivered as services running inside your infrastructure, so documents are processed within your network boundary and do not need to be sent to external multi-tenant environments. This approach aligns with internal IT standards, data residency policies, and regulated workloads while leveraging your existing compute, storage, and GPU resources.

Yes. The platform can run entirely within your network boundary with no dependency on external services.

All processing components, including document ingestion, classification and extraction models, validation, APIs, and storage, are deployed inside your infrastructure. Documents remain within your environment, and the system can be configured with no outbound internet access, making it suitable for highly regulated or air-gapped environments.

For customers using our managed offering, secure access into your environment is required. This access enables us to monitor performance, identify and resolve issues, and retrain models as needed.

No. Your data does not need to be sent to any third-party services for document processing.

All classification, extraction, validation, and storage can run entirely within your own environment, whether on-premises or in a private cloud, ensuring documents remain under your control at all times. The platform is self-contained and does not rely on external APIs or multi-tenant AI services for core processing.

If you choose to enable optional integrations, such as connections to downstream systems or external storage, they are explicitly configured by you and operate under your security and networking policies. Otherwise, the system can run with no outbound internet access, making it suitable for restricted and regulated environments.

This approach preserves full data ownership, simplifies compliance, and reduces the risk of sensitive information being exposed to external providers.

Our IDP solution integrates with your existing workflows and systems to automatically classify and extract data as documents are ingested, eliminating the need for manual sorting.

Using machine learning models trained on your document sets, the platform identifies document types within mixed batches and multi-page files, and groups related pages together. It supports structured, semi-structured, and unstructured documents, including invoices, remittances, tax forms, mortgage packages, and correspondence.

Each classification includes a confidence score and can be validated against your business rules. Low-confidence items are automatically routed to an exception queue for review, while high-confidence documents proceed through straight-through processing into extraction and downstream systems.

This enables true hands-off intake, faster processing, and consistent, auditable document separation at scale.

Yes. The platform is designed to handle multi-page and mixed-document packets and can automatically determine which pages belong together.

As files are ingested, the system analyzes page content, layout, and context to separate, group, and classify documents within a single batch. This works even when document lengths vary or when packets contain a mix of structured and unstructured documents.

Once grouped, each document is processed as a complete unit for accurate extraction and validation across all pages. The system preserves page order, supports cross-page field detection, and returns results with full traceability and confidence scoring.

This enables high-volume, hands-free batch processing without manual document separation.

Yes. The platform is designed to process structured, semi-structured, and fully unstructured documents.

For semi-structured documents such as invoices, remittances, and forms, the system uses layout and contextual cues to locate and extract the correct fields, even when formats vary between vendors or versions.

For unstructured content such as correspondence, statements, emails, and narrative reports, the models analyze language and document structure to classify the document, identify relevant data, and return normalized, labeled output.

Because extraction is driven by machine learning rather than rigid positional rules, the solution remains accurate as layouts change, new formats are introduced, or multiple document styles appear in the same batch. Low-confidence results can be automatically routed for review, enabling reliable automation across real-world document variability.

There is no fixed limit to the number of document types the platform can support. In practice, the number is driven by your business needs rather than by any platform constraint. In a single deployment, we have successfully processed more than 10,000 document types organized in a hierarchical structure. Many of these differed only slightly, such as by year, yet we achieved classification accuracy of approximately 98 percent. Additional document types can be supported as needed, with appropriate process optimization for scale.

Each document type is onboarded through model training and configuration tailored to the required fields, structure, and business rules. New types can be introduced incrementally without disrupting existing production workflows. Because the classification and extraction models are modular and versioned, expanding coverage does not require retraining the entire system. This enables you to start with a focused, high-value document set and scale over time as new use cases are added.

Throughput is determined by the infrastructure you allocate and the complexity of your documents, but the platform is built for high-volume, production-scale processing. With GPU acceleration and parallel pipelines, it can process from thousands to millions of pages per day while maintaining consistent classification and extraction performance.

The architecture is horizontally scalable, allowing capacity to increase by adding worker nodes without redesign or downtime. Each processing component runs as an independent service that can scale across your environment, enabling ingestion, classification, extraction, and validation to operate concurrently at high throughput.

For peak demand, the system supports elastic scaling and queue-based workload management. Additional compute resources can be brought online to absorb volume spikes and scaled back when demand normalizes. Because deployments run within your environment, scaling aligns with your infrastructure policies, performance requirements, and SLAs while maintaining full data control.

Throughput can also be improved through code and process optimization, GPU enablement, caching, intra-process parallelization, and extended processing windows. For example, workloads can be queued and executed outside normal operating hours to increase capacity without additional infrastructure cost.

Yes. We use a combination of pretrained and custom-trained models tailored to your specific document types, fields, and business rules, ensuring the solution is optimized for your real production data.

The amount of training data required depends on the complexity and variability of the documents. For many semi-structured documents, a few dozen to a few hundred well-representative samples are sufficient to achieve strong accuracy. More complex or highly variable documents may require additional examples, but we follow an incremental approach, starting with a small, high-value dataset and improving performance as more samples become available.

Document type setup, dataset creation, and data labeling are currently managed by IDPS to ensure the solution is configured correctly and performs optimally from the start. This structured, quality-controlled process allows us to identify potential issues before production. All training is performed within your environment using your data.

Accuracy is measured at both the page and field levels, and expected performance depends on the document type, source quality, and layout variability. For well-defined, high-volume documents, organizations typically achieve high straight-through processing rates, with most fields extracted at a level suitable for fully automated downstream use. During onboarding, we establish clear, measurable acceptance thresholds based on your business and compliance requirements. In most cases, we target approximately 98 percent accuracy.

Each classified page and extracted value is returned with a confidence score and full traceability, giving you precise control over what is automated and what requires review. Confidence thresholds can be configured by document type and by field. For example, totals above a defined confidence level can be posted automatically, while more sensitive fields are routed for validation.

Low-confidence results are automatically directed to an exception workflow rather than entering core systems. Reviewers see the document, classifications, extracted values, and the model’s suggested locations, enabling fast and accurate correction. These corrections can be fed back into the training cycle to continuously improve performance over time.

This approach maximizes automation while maintaining accuracy, auditability, and operational control.

Yes. The platform is designed to integrate seamlessly with your existing ECM, ERP, RPA, capture, and scanning environments so that extracted data flows directly into your operational workflows.

Integration is typically achieved through REST APIs, message queues, secure file exchange, or database connectors. This allows us to ingest documents from upstream systems and deliver structured, validated output to downstream applications. Common use cases include digital mailroom intake, automated invoice posting, mortgage LOS updates, and triggering RPA bots with system-ready data.

We work within your current architecture rather than requiring replacement. Documents can be received from scanners and capture platforms, processed through the IDP pipeline, and returned in the format your systems expect, such as JSON, XML, CSV, or direct system updates. Authentication, transport, and networking are aligned with your internal IT and security standards.

During the discovery phase, we assess your requirements, constraints, and integration points, then design and implement the appropriate connectors. Reasonable integration and development efforts are included in the pricing because our goal is to ensure project success without unexpected costs.

This approach enables you to add intelligent document automation with minimal disruption while accelerating end-to-end processing.

Implementation is structured, fast, and aligned to your production workflow. We begin with a short discovery phase to confirm document types, fields, volumes, success criteria, and integration points. From there, we deploy the platform in your environment, configure ingestion and output connections, and train the initial models using representative samples. The result is a pilot or production-ready workflow that is validated against real documents and measurable acceptance thresholds—not just isolated accuracy tests.

Once in production, the system is supported through monitoring, model versioning, and performance reviews. You receive visibility into throughput, automation rates, confidence levels, and exception volumes so performance can be continuously optimized. As new document types, format changes, or business rules are introduced, we update models and configurations without disrupting existing workflows.

Ongoing support is flexible to match your operating model. We can provide hands-on managed support, collaborate with your internal team, or transition day-to-day operation to your staff with training and clear governance. In all cases, you have defined SLAs, a clear escalation path, and a structured process for enhancements, ensuring the solution evolves with your business while maintaining production stability.

We also offer an unmanaged deployment option. After the initial onboarding and configuration, ongoing support, model retraining, monitoring, and related services are available as needed for an additional cost. This approach is best suited for customers with a small number of stable document types that change infrequently, and it provides a more cost-effective option for those use cases.