In a world where information grows exponentially and companies handle huge volumes of documents every day, the ability to access with precision, speed and context the knowledge contained in those documents becomes a competitive advantage. In this scenario, Retrieval Augmented Generation (RAG) technology emerges as a disruptive and transformative solution.
Retrieval Augmented Generation or RAG is an advanced artificial intelligence technique that combines two key capabilities:
The fundamental difference with traditional generative models is that Retrieval Augmented Generation or RAG is not based solely on previously trained data, but queries current, specific and contextual information from the company’s own documentary sources, improving accuracy and reducing the risks of erroneous answers or “hallucinations” typical of some AI models.
Traditional document management platforms (ECM, DMS) focused on storing, organizing and controlling access to documents. However, in many cases, information retrieval is still manual, slow, and highly dependent on metadata, filters or prior knowledge of the system by the user.
Retrieval Augmented Generation or RAG changes the rules of the game by allowing users to interact with the knowledge contained in documents in a conversational, natural and intelligent way. Among its main benefits are:
Retrieval Augmented Generation or RAG provides a transformative layer of intelligence by allowing users to interact with content through natural language and obtain document-based answers in seconds.
| Type of search in volumes greater than 100K | Metadata search | Full-text search | RAG (Retrieval Augmented Generation) |
| Accuracy | Good accuracy, if the documents are properly indexed. Disadvantage: metadata work must have been done beforehand. | It can achieve acceptable accuracy but requires well-structured and performing indexes. It is usually much lower than the accuracy achieved with metadata. Requires prior extraction of text from documents, with its consequent cost. | Fairly accurate, but depends on the amount of information sent to the LLM. The more information, the more accurate. |
| Speed | Good response time as long as the system has a performing search backend. | Slow. This type of searches are not very performant. | Very good, although it will be affected by the amount of information sent to the LLM. |
| Usability | It is less comfortable for the user because he has to know in which metadata is the information that will help him to find the content and because he has to “build” the query. That is, it requires prior knowledge of the information and how to access it. The user will have to review the documents returned by the search and read the document(s) to understand and be able to use the information obtained. | It is convenient for the user because he does not need to know exactly which fields to search in, just type in words that he thinks will appear in the content. Searches are almost literal. For example, you can search for the word “dog” and use wildcards that also return documents that include “dogs”. But the search will not return documents that say “hound” or “can”. The user will have to review the documents returned by the search and read the document(s) to understand and be able to use the information obtained. | It is very convenient for the user because he can ask as he wants. It adds the semantic component that full-text does not have. For example, if you search for “documents that talk about dogs” you will get those that not only include the word “dog”, but also those that mention “canines” or “hounds”. Searches are further enriched with reasoning or interpretation of the information. The LLM can synthesize and analyze the information obtained, facilitating consumption and reducing the need for reading. |
| Traceability | Good. Normally the systems will return the list of documents where there is a search match. | Good. Normally the systems will return the list of documents where there is a search match. They may also show you where in the text of the document the data appears. | He can give you the reference or references he used to answer you and tell you where he is. |
Retrieval Augmented Generation or RAG technology is already being successfully applied in multiple sectors and industries. Some outstanding examples:
Companies with tens of thousands of regulatory documents and processes can offer their employees a conversational interface to resolve questions without resorting to compliance or internal control areas.
Example: “Which policy applies to teleworking for the IT department?” returns a clear answer based on the corresponding updated document.
Customers can resolve their doubts about products, contracts or conditions directly from a RAG chatbot that consults the actual documentation, reducing service times and improving the experience. In turn, it reduces the burden on the contact center and brings accuracy to responses.
Legal, risk or audit areas can directly ask questions about contracts, internal policies or current regulations, obtaining substantiated and verifiable answers for critical compliance processes.
In the public sector, state, regional and local agencies manage large volumes of rules, regulations and administrative procedures. Many departments devote significant resources to manually answering repetitive queries from citizens and public employees about internal operations, procedures, deadlines, conditions or applicable regulations.
With Retrieval Augmented Generation or RAG, it is possible to transform this regulatory repository into an intelligent knowledge base, accessible through natural language and without direct human intervention. Both citizens and officials can obtain clear and traceable answers on procedures, legal frameworks or operational instructions directly from the official documentation.
Example: A citizen could query “What are the requirements to apply for a rental subsidy in my city of Malaga?” and receive an immediate answer based on current regulations, without having to navigate through long pages or wait for telephone support.
This drastically reduces the volume of manual attention, improves the public service experience, and ensures that the information provided is consistent, up-to-date and based on official sources.
Retrieval Augmented Generation or RAG turns document repositories into living knowledge bases: R&D reports, market analysis, financial reports or internal presentations can be transformed into accessible answers for multiple departments.
RAG allows you to quickly extract technical knowledge from reports, papers, patents or internal presentations for R&D or innovation areas.
Financial institutions or insurers use RAG to consult current commercial conditions, access regulatory documentation, respond to audit requirements or address internal queries about financial products and insurance policies in real time.
Traditionally, document search relied on keywords, rigid structures or metadata. With RAG, users can search in natural language across all documentary content without worrying about how a file was classified or named.
Example: “Where are the terms of business approved in March for healthcare clients?” will return an answer based on the actual documents, even if that phrase does not appear verbatim in the texts.
This speeds up processes, improves accuracy and reduces the learning curve for users by not requiring knowledge of the document manager structure.
Retrieval Augmented Generation or RAG doesn’t just retrieve documents: it understands and adapts them to the company’s logic, jargon and priorities. This provides more accurate answers, aligned with the accumulated internal knowledge.
For example, when asked the question “How do we manage short-term sick leave?”, the answer will take into account specific internal policies, current annexes and country-specific regulations, without requiring the user to know exactly which document to consult.
In addition to the core use cases, Retrieval Augmented Generation or RAG technology applied to document management brings additional benefits that directly impact operational efficiency, regulatory compliance and team autonomy:
RAG allows access to current information only, as responses are generated in real time from the most current documents available in the system. This minimizes the risk of users consulting outdated versions of policies, contracts or procedures, a common problem in environments with high document turnover.
With full traceability of responses, each query made using RAG can be linked directly to the source document, indicating date, location and context. This is essential in regulated environments, where it is imperative to demonstrate the source and validity of information used in internal decisions or communications.
Legal and commercial departments can use RAG to query specific clauses, contractual terms or internal regulations without the need to escalate every query to an in-house lawyer or compliance expert. This relieves operational burden on specialized teams and accelerates informed decision making in critical processes.
While other platforms are still exploring how to integrate AI, Athento has become the first document management platform to offer RAG as a native, out-of-the-box functionality.
Leading companies are already using Athento to transform their document management into organizational intelligence:
The technology of Retrieval Augmented Generation or RAG represents a before and after in the way organizations access, understand and exploit their document knowledge. We are moving from a model based on passive storage to one where information becomes active, accessible and strategic knowledge.
In this new paradigm, Athento leads the way as the first ECM platform to offer RAG natively, without technical barriers or external dependencies. Our pioneering approach democratizes access to document artificial intelligence and opens the door to a new era where conversation with documents is as natural as it is useful.
Athento already makes it possible. And you, what are you waiting for to take the leap? Contact us.
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