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Turn your documents into intelligent knowledge with AI

  • Introduction
  • Benefits
  • Use Cases
  • AI Architecture and Integration
  • Pricing and enquiries

Introduction

Intelligent Document Management with AI

Document management is no longer just about storing and searching for files. Today, organizations need to understand what their documents contain, leverage that knowledge, and automate processes by relying on Artificial Intelligence.

OpenKM addresses this need with an intelligent document management platform that combines:

  • a mature document and records management system (DMS),
  • a workflow engine and automatic tasks,
  • and a flexible AI layer, connectable to different providers and models, both in the cloud and in on-premise environments.

From the outset, OpenKM has been designed with a clear premise: AI must adapt to the customer’s data strategy, not the other way around.

That is why the platform:

  • provides AI connectors based on standard APIs,
  • can work with any compatible model (language models, classification models, semantic search engines, etc.),
  • Pand allows these capabilities to be deployed in public cloud, private cloud or on-premise environments, according to the security, compliance and data sovereignty requirements of each project.

On this basis, OpenKM is not just another tool that “calls” an external AI; it integrates AI directly into its document management logic. Information is organized with folders, taxonomies and metadata; controlled with permissions, auditing and a file plan; and, on top of all that, AI:

  • understands the content,
  • links related but scattered documents,
  • provides answers in natural language,
  • and triggers automations when it detects business conditions.

The result is AI-powered document management in which users stop “searching for documents” and start searching for answers, working over a true corporate knowledge base.

Benefits

Why choose intelligent document management with OpenKM

The combination of OpenKM + AI delivers tangible benefits at different levels of the organization and turns your DMS into an enterprise knowledge management platform.

Fast access to knowledge

  • Documentation is no longer just a simple repository; it becomes a queryable knowledge base.
  • Users ask questions in natural language (“Which procedure applies in this case?”, “What documentation do I need?”) and receive answers based on internal documents.
  • Time spent searching for information scattered across folders, emails or isolated systems is drastically reduced..

With intelligent document management, OpenKM makes it easier for teams to find the right information at the right time.

Operational efficiency and fewer repetitive tasks

  • AI helps capture data (advanced OCR), classify documents and suggest metadata, which reduces manual work.
  • Workflows and automatic tasks rely on this information to route documents, launch approvals, send notifications or apply business rules.
  • Teams can spend more time analyzing and deciding, and less time locating and copying information.

In short, AI-powered document management increases productivity and accelerates everyday processes.

Quality and consistency in processes

  • By working with a central repository and consistent rules, the organization achieves a homogeneous interpretation of policies, manuals and procedures.
  • AI assistants can support less experienced users, guiding them with the same business logic defined in the official documentation.
  • This leads to fewer errors, fewer reworks and a better experience for both internal and external customers.

Your enterprise knowledge base stops depending on “who knows what” and becomes a shared, reliable resource.

Security, compliance and data governance

  • The entire AI layer is built on top of a document management system with granular permissions, user and role management, detailed auditing and a file plan.
  • It is possible to decide which document sets are exposed to each AI model, what information is anonymized and which data never leaves the local environment or private cloud.
  • This approach makes it easier to comply with regulations such as GDPR and with internal security and corporate governance requirements.

You get intelligent document management without sacrificing control over sensitive information.

Technological freedom and future-proofing

  • Thanks to open connectors, OpenKM can work with different AI engines and providers, without locking the customer into a single technology.
  • As new models or services appear, they can be incorporated without changing the underlying document management platform.
  • Investment in OpenKM thus becomes a stable foundation on which the organization’s AI strategy can evolve over time.

OpenKM positions itself as an AI-ready document management system, prepared to grow with your business and your technology choices.

Use Cases

What AI can do with your documentation in OpenKM

OpenKM’s intelligent document management can be applied to many areas. Some of the most common use cases are:

Intelligent capture (OCR + AI)

  • Digitization of invoices, contracts, delivery notes, forms or files.
  • Advanced OCR to extract text, even from scanned documents or photos.
  • Field recognition and AI-based validation: automatic identification of supplier, amount, dates, internal references, etc.
  • Direct integration with approval workflows and external systems (ERP, accounting, CRM).

Benefit: less manual typing, fewer errors and greater speed in back-office processes.

Automatic classification and intelligent metadata

  • Identification of the document type (invoice, quote, contract, report, complaint, internal manual, etc.).
  • Automatic suggestion of relevant metadata (customer, project, department, product, geographic area, etc.).
  • Application of intelligent rules that use this metadata to move the document to the right place, assign permissions or start a workflow.

Benefit: coherent document structures with very little effort, and much more effective subsequent searches.

Intelligent search and natural-language queries

  • The user doesn’t need to know the exact title of the document; they can ask natural-language questions (“What does the policy say about this topic?”, “How do we handle these incidents?”).
  • AI locates the most relevant fragments (semantic search) and returns a written answer, with links to the source documents.
  • This approach, based on RAG (Retrieval-Augmented Generation), always relies on internal documentation, not on uncontrolled generic knowledge.

Benefit: speed and confidence when making decisions based on policies, manuals and technical documentation..

Virtual assistants over internal documentation

  • Creation of specialized virtual assistants that work on the OpenKM repository:
  • internal user support assistants,
  • assistants to consult compliance manuals,
  • assistants to help with internal procedures.
  • They can be integrated into OpenKM itself, the corporate portal or collaboration tools (intranet, chat, etc.).

Benefit: expert knowledge available 24/7, without overloading support or compliance teams.

Intelligent automation of document processes

  • Workflows that use AI to decide the next step: classification, approval, routing to a specific area, request for additional information.
  • Automatic tasks triggered when certain content appears (for example, specific terms in a contract, specific amounts or conditions in a financial document).
  • Integration with customer service and quality tools to link tickets, complaints and supporting documentation.

Benefit: faster processes, less friction and complete traceability of who did what, when and why.

AI Architecture and Integration

How Artificial Intelligence integrates with OpenKM

OpenKM’s proposal is based on an open, flexible architecture, designed so that each organization can choose its own AI engines and control where data is processed.

AI connectors and open APIs

  • OpenKM exposes REST APIs and SDKs that facilitate integration with generative AI providers, semantic search engines, advanced OCR services and other intelligent components.
  • Communication uses standard protocols (HTTP/JSON), which makes it possible to connect with different current and future services.
  • This same architecture supports both models hosted in public clouds and models deployed in the customer’s own infrastructure.

In practice, OpenKM becomes the hub where your AI models and your document management system meet.

AI in local, private cloud or hybrid environments

  • For projects with high security and confidentiality requirements, AI can run in on-premise environments or private clouds, without moving documents outside the organization’s perimeter.
  • OpenKM can also work with AI services in the public cloud, limiting and controlling what information is sent and how it is anonymized when necessary.
  • In hybrid scenarios, part of the capabilities (for example, heavy OCR processing) can reside in the cloud, while more sensitive processes (such as querying critical internal documentation) are handled by local models.

This flexibility allows you to design the AI deployment that best fits your security and compliance needs.

From repository to knowledge base (RAG – Retrieval-Augmented Generation)

At the logical layer, OpenKM uses techniques such as RAG (Retrieval-Augmented Generation) to turn the document repository into a knowledge base:

1. Indexing and embeddings

  • Document content is analyzed and transformed into vector representations to enable semantic search.

2. Retrieval

  • When a question is asked, the system selects the most relevant fragments from the internal documentation.

3. Generation

  • A language model uses those fragments to draft an answer in natural language, always preserving the link to the original documents.

All this happens under OpenKM’s security, permission and auditing rules, ensuring that AI-powered answers are both useful and governed.

Integration with workflows and automatic tasks

  • AI results are not left “floating”: they can feed workflows (OKMFlow) and automatic tasks.
  • For example, a document classified by AI as a “contract with clause X” can trigger a specific legal review workflow.
  • Decisions and actions are recorded, contributing to process traceability and governance.

In this way, intelligent document management becomes a real driver of end-to-end process automation.

Pricing and enquiries

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