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OpenKM Intelligent Search: applying RAG to document management systems

Written by Ana Canteli on 18 november 2025

When the pace of change in the digital space does not stop accelerating, the amount of information handled by organizations keeps growing without end. Every year, more contracts, internal policies, emails, reports and technical documentation are generated. Traditional document management systems are good at storing and organizing files, but they often fall short when it comes to finding the specific information, at the exact moment, with the speed that users expect today.

The result is very familiar in many companies: users who browse through too many folders, try combinations of keywords, copy and paste document links, and open several files at the same time… just to answer a single question.

To close this gap, at OpenKM we have incorporated an intelligence layer based on RAG (Retrieval-Augmented Generation) and we have packaged it as a solution ready for your business: OpenKM Intelligent Search.

Powered by generative AI, OpenKM Intelligent Search actively analyzes internal information repositories — in different formats and sources — and dynamically shows the most relevant content for each query, in the form of a natural language answer. Instead of forcing the user to “find the right document”, the platform delivers the answer directly, with traceability back to its sources.

This is especially powerful in environments that are:

• intensive in documentation,
• highly regulated,
• where precision and regulatory compliance are critical.

In that context, criteria such as compliance, criticality and operational efficiency make the difference.

What is RAG and how is it translated into OpenKM Intelligent Search?

RAG: an AI framework placed at the service of the business

RAG is an artificial intelligence approach that combines two capabilities:

• Information retrieval: It retrieves useful content from a knowledge base: in this case, the documents stored in OpenKM (policies, contracts, manuals, emails, etc.).
• Generative model (LLM, Large Language Model): It uses a large language model to transform that information into an answer written in natural language, adapted to the context of the query.

Whereas a “pure” generative model can only rely on what it learned during its training, RAG grounds the answers in real documents of the organization. That is: it does not answer “from memory”, it answers by citing your own corporate knowledge.

Explained without technicalities

When a user asks a question in OpenKM Intelligent Search, something similar to this happens:

1. The system interprets the meaning of the query: It does not focus only on the exact words, but on what the user wants to know (for example: “vacation policy for part-time employees”).
2. It compares that intent with the content of the documents: OpenKM has already previously analyzed the documents and has converted them into numerical representations that capture their meaning (this is what is technically called “embeddings”). In practice, this allows it to search by ideas, not only by exact words.
3. It retrieves the most relevant fragments: It does not bring back only “the document”, but exactly the sections that best answer the question.
4. It generates a natural language answer: A language model (LLM) writes a clear and brief answer, relying on those fragments, and includes references or links to go to the original document in OpenKM.

Why is it especially relevant for document management?

This approach offers three key advantages in a document management platform like OpenKM:

• More precision and fewer “hallucinations”: The answers are based on specific documents. The model does not “invent” content, but uses the knowledge stored in the organization.
• Always up-to-date information: OpenKM Intelligent Search works on the live documents in the repository, so it always takes into account the latest versions.
• Natural language search: Users can ask as they would ask a colleague: “What is our travel policy for suppliers?” without having to build advanced searches or know the folder structure.

How RAG transforms the OpenKM platform: architecture of OpenKM Intelligent Search

Applying RAG on top of OpenKM turns the document management platform into an intelligent knowledge center.

In a simplified way, we can understand the architecture of OpenKM Intelligent Search in four layers:

Ingestion and indexing of documents

• The OpenKM documents (PDF, Word, emails, etc.) are analyzed and converted into those numerical representations that capture their meaning.
• That information is stored in an index optimized for semantic search.

Retrieval layer

• The user formulates a question.
• The system performs a semantic search across the entire OpenKM repository, without depending on file names or the folder structure, to find the most relevant fragments.

Generation layer

• An LLM uses those fragments as “base facts” and builds a cohesive answer: summaries, steps, lists, comparisons, etc.

Traceability and governance

• Each answer includes references or links to the relevant documents in OpenKM, key for audits and legal or regulatory compliance.
• In addition, OpenKM Intelligent Search always respects access permissions (RBAC): the system only returns information to which the user already has access through their roles.

Organizations can also:

• Specialize OpenKM Intelligent Search by domain: legal, healthcare, financial, etc.
• Deploy the solution on-premise or in private infrastructures, keeping sensitive data under their own control.

Use cases of OpenKM Intelligent Search

Management of policies and procedures

Typical problem: HR maintains dozens of documents: remote work, sick leave, travel expenses, data protection, etc. Employees ask:

“What is our remote work policy for part-time employees?”

• Without OpenKM Intelligent Search: The employee searches by keywords, opens several PDFs and goes through pages until finding the correct paragraph… if they are lucky that it is the latest version.
• With OpenKM Intelligent Search: The employee submits the question in a chat-type interface inside OpenKM. The system:
  • locates the updated versions of the HR policies,
  • extracts the relevant sections,
  • and generates a clear answer with links to the specific sections of each document.

Result: Less time wasted searching, fewer errors from using old documents and coherent messages throughout the organization.

Contract lifecycle management

Scenario: The legal department uses OpenKM to manage all contracts, but reviewing specific clauses (termination, liability limits, renewals…) is slow.

• With OpenKM Intelligent Search: A lawyer can ask: “Show me the termination clauses of our 10 main contracts with suppliers.”The system:
  • locates the relevant contractual sections in OpenKM,
  • generates a comparison of notice periods, penalties and renewal conditions,
  • adds direct links to each contract to review the full context.

Business benefit: Due diligence, renegotiation and risk assessment are accelerated, always relying on the actual contractual text.

Compliance and audit preparation

In regulated organizations, compliance and audit teams must continuously check whether their internal documents are aligned with standards such as GDPR, ISO or other sector regulations.

• With OpenKM Intelligent Search integrated: A compliance officer can ask; “Which documents govern our data retention policies and are aligned with the latest GDPR specifications?”.The system:
  • locates policies, procedures and legal notes stored in OpenKM,
  • generates a summary,
  • identifies old or contradictory documents.

In an audit, it can answer queries such as: “Give me all the records related to our data protection audit for the second quarter of 2025.” and return the set of relevant policies, records and contracts, ready for review.

Result: Less time spent preparing documentation, lower risk of omitting evidence and greater confidence from the auditor.

Sharing knowledge, collaboration and intelligent summaries

OpenKM Intelligent Search turns the platform into an internal knowledge assistant:

• Project teams can ask: “Give me the key points of the latest quarterly sales report.”
• Newly onboarded people can ask: “How do we register a new customer in our CRM?”

Instead of having to read long documents, users receive:

• a summary of the relevant documents,
• and the option to open the originals for more detail.

Business benefits of OpenKM Intelligent Search

For companies that already use OpenKM (or that are considering adopting it), adding this intelligence layer brings very clear benefits:

• Productivity improvement: Less time searching for information, more time analyzing and making decisions.
• Better decision-making: The answers are based on real and up-to-date documents, which reduces the risk of decisions supported by incomplete or outdated information.
• Reinforced compliance: It is possible to trace each answer back to the original documents, which facilitates audits and internal controls.
• Lower learning curve: Users do not need to know the structure of the repository or master complex search syntax: it is enough to formulate the question in natural language.

Where RAG is going in OpenKM: evolution of Intelligent Search

Some trends that will continue to strengthen OpenKM Intelligent Search:

• Multimodal RAG: Search and inference beyond text: images, scanned PDFs, diagrams or even video transcripts. Especially useful for technical documentation in sectors such as construction or healthcare.
• RAG agents (agentic RAG): AI “agents” that not only answer questions, but can execute multi-step workflows: classify documents, suggest tags, generate draft proposals based on OpenKM content, etc.
• Private and edge deployments: Running all the search intelligence within the organization’s own infrastructure, keeping sensitive documents always under control (finance, public sector, healthcare).
• Adaptive RAG models: Systems that improve with use: they learn from what users consult, correct or ignore, adjusting the relevance of the results to the reality of the business.

Conclusion: from document manager to strategic asset

In its most advanced version, Retrieval-Augmented Generation fits naturally with document management systems. Integrated into OpenKM as OpenKM Intelligent Search, this approach:

• combines powerful retrieval techniques with generative AI,
• offers access to organizational knowledge in a precise, contextual and explainable way,
• and turns a document repository into a true strategic asset.

OpenKM ceases to be just “a place where files are stored” to become a platform that:

• understands questions in natural language,
• finds the most relevant content,
• produces clear answers with sources,
• and allows every detail to be verified in the original documents.

If you want to explore how OpenKM Intelligent Search can help your organization get more value from its information, we will be delighted to talk with you and show you practical examples adapted to your sector.