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OpenKM + AI (RAG): turning hundreds of manuals into a knowledge base in the financial sector

Written by Ana Canteli on December 05, 2025

In many organizations in the financial sector, the compliance area manages hundreds of manuals, policies and internal procedures. The knowledge exists, but it is scattered across PDF and Word documents and presentations that are hard to consult in day-to-day work.

At the same time, management is asking a very concrete question:

“Can we have a corporate AI, based on Retrieval-Augmented Generation (RAG), that runs in a local or private environment, without exposing information to external services, and integrated with our document management system?”

This is precisely the scenario where OpenKM + AI (RAG) fits: turning a compliance document repository into a corporate knowledge base, powered by a RAG system that offers natural-language answers based on the organization’s actual documentation, always working with up-to-date information inside a controlled environment.

What is RAG and why is it key in the financial sector?

RAG (Retrieval-Augmented Generation) is an AI architecture that combines two stages:

1. RAG retrieval (information retrieval)

The system locates the most relevant fragments in a data source; in this case, the compliance manuals and internal documentation stored in OpenKM. We are talking about a RAG architecture with semantic search, which uses a vector database to find relevant information even when the user’s query is phrased in different words from the original text.

2. Augmented generation

A generative model from the family of large language models (LLMs) takes those fragments and generates a response in natural language. In this way, the artificial intelligence produces coherent answers backed by the organization’s internal knowledge.

In practical terms, a corporate RAG system means that users no longer need to open the manuals one by one, but can ask complex questions such as:

• “For regulatory compliance, what should I do if a customer refuses to provide KYC documentation?”
• “What is the protocol for reporting a suspicious transaction in my country?”

RAG retrieval finds the relevant fragments (not only by keyword, but by meaning), and retrieval-augmented generation builds a clear, more precise answer aligned with internal procedures. This is what is known as RAG (Retrieval-Augmented Generation) applied to knowledge management and knowledge bases in the financial sector.

OpenKM as a document management platform and compliance knowledge base

Before AI can change the way people ask questions, documents must be put in order. This is where OpenKM behaves as a true document management and knowledge management system:

• A file plan and records management for compliance manuals.
• Version, scope, regulation and expiry metadata, among others.
• Granular security by user, role, group and document, useful for corporate governance and for enforcing the privacy policy and privacy notice.
• Subscription services and notifications whenever a manual is updated or a new internal policy is published.

All this information is stored in corporate databases and becomes a large compliance dataset. OpenKM does more than just store documents: it builds a structured knowledge base, ready for a RAG system to use as a knowledge source and data source.

How the RAG architecture works with OpenKM in a local or private environment

On top of this document management core, OpenKM can implement RAG following a very clear architecture, designed to run in a local environment or private cloud, with no access to external information by default:

Indexing and vector database

Hundreds of compliance manuals, corporate governance guidelines, risk policies, customer service documentation and other content are processed using natural language processing.

From each document, vector representations are extracted and stored in a vector database. This enables information retrieval by meaning, not just by exact keywords.

RAG retrieval on OpenKM

When a user query arrives—where regulatory compliance is often complex (for example, combining product, channel and country)—the RAG retrieval engine searches the knowledge base for the most relevant fragments.

RAG models can retrieve content from internal knowledge (manuals, policies, instructions) and, if the organization decides in a controlled way, incorporate some external knowledge (for example, summaries of public regulations).

All of this happens within the local or private environment defined by the institution, without sending compliance manuals or other sensitive documents to external services. The organization decides whether it wants to connect to external data or not; by default, the RAG system works exclusively with its own internal data source.

Language models and retrieval-augmented generation

The retrieved fragments are passed to the language models (the underlying generative AI models), which, combining the relevant data with their training data, produce results in the form of more precise and coherent answers, already contextualized for the institution’s banking / financial sector environment.

Retrieval-augmented generation (RAG) ensures that the AI does not make things up: it always relies on real documentation and up-to-date data in OpenKM, using the organization’s own knowledge base.

Answer automation and virtual assistants

On top of all this, an internal virtual assistant can be published: the user asks a question, the RAG system performs the retrieval step on the compliance manuals stored in OpenKM and, based on that information, the AI generates a natural-language answer.

This answer automation improves operational efficiency, reduces search time and enhances the experience of teams working on internal processes and also on customer interactions.

Compliance use cases: complex queries, generated answers

For a financial sector institution with hundreds of compliance manuals, a RAG system integrated with OpenKM enables:

• A compliance analyst to ask complex questions about a specific operation and receive a clear answer based on current compliance policy and privacy policy.
• A customer service manager to access specific information about complaints procedures, pre-contractual information or data protection, without needing to read dozens of pages every time.
• The corporate governance area to quickly check which obligations apply to a new financial product, combining internal data and, optionally, some controlled external data.

In all these cases, RAG retrieval operates on the manuals and internal documentation stored in OpenKM, and retrieval-augmented generation ensures that the AI produces an understandable answer that aligns with the regulations and with the organization’s internal datasets.

Competitive advantages of the RAG approach in OpenKM

Clear competitive advantages

• Moving from “searching for documents” to providing more precise answers on regulatory compliance, reducing human errors and search times.
• RAG models and generative AI models can retrieve content scattered across multiple manuals and combine models so that the document management system generates a useful, verifiable answer.

Greater control over internal knowledge

• The content in OpenKM becomes a living, up-to-date knowledge base.
• The financial institution decides which knowledge sources are included in the RAG system, and how security, privacy and data governance are handled, in line with its own internal policies and external regulations.

Better experience and operational efficiency

• Users receive generated answers in natural language, instead of having to interpret long regulations by themselves.
• This translates into operational efficiency, fewer repetitive queries to the compliance area and more agile support for business and customer service teams.

Technological flexibility and deployment in local or private environments

• OpenKM can be deployed on standard Linux servers, including enterprise distributions such as Red Hat, on private clouds or in hybrid environments.
• It is possible to implement RAG with different language models and base models (from different AI providers) without losing control over the data source or the compliance manuals, keeping processing in a local private environment with no default access to external information.

Conclusion: OpenKM as a document management + RAG platform for the financial sector

In summary, OpenKM enables organizations in the financial sector to transform their compliance manuals into a true governed knowledge base, on top of which a corporate RAG system can be built:

• with retrieval-augmented generation applied to their internal knowledge,
• where RAG retrieval and augmented generation work together to provide answers and more precise answers based on the organization’s real documentation,
• and where the language models integrated into the RAG architecture always operate in a local or private environment, with up-to-date information and under the institution’s own rules for security, corporate governance and privacy (including its privacy policy).

The combination of knowledge management, knowledge bases and RAG (retrieval-augmented generation) makes OpenKM much more than a document manager: it becomes the platform on which artificial intelligence revolutionizes the way financial sector professionals access critical information and apply it in their daily work, with a clear focus on control, security and compliance.