RAG for SMEs: How to Make AI Answer with Your Company's Information

If you have ever asked ChatGPT or any AI assistant about your company, you have noticed that the answers are generic. It knows a lot about the world, but nothing about you. It doesn't know your products, your internal processes, your return policies, or the name of your main supplier. And that makes sense: it doesn't have access to that information.

RAG (Retrieval-Augmented Generation) solves exactly that problem. It is the technique that allows the AI to consult your company's actual documents before answering, so that the responses are based on your information and not on general knowledge.

In our previous article on MCP servers, we explained how to connect AI to your company's data without exposing it to third parties. RAG goes a step further: it doesn't just connect, but it searches for and selects the relevant information for each specific question.

What is RAG, explained without jargon

Imagine hiring a new, very intelligent employee who knows nothing about your company. Every time you ask them a question, they have two options: invent the answer based on what they know about the sector, or consult the internal manuals and documents before answering.

RAG is the second option. You give the AI access to a company internal search engine. When someone asks a question, the AI first searches your documents for the most relevant fragments, reads them, and then generates an answer based on that real information.

The flow is simple:

1. A user asks a question
2. The system searches your documents for the most relevant fragments
3. The AI receives these fragments as context
4. It answers with real information from your company, not generalities

Why simply copying and pasting documents into the prompt is not enough

The first reaction of many companies is to think: "Well, I'll just paste my documents into the chat and I'm done." But that has real problems:

• Capacity limit: you cannot put 500 pages of manuals into every conversation. AI models have a context limit, and even though it is constantly increasing, filling it with entire documents is inefficient.
• Cost: more text in every query means higher cost per request. If you have a chatbot that receives hundreds of questions a day, the bill skyrockets.
• Noise: if you put everything in, the AI has to find the needle in the haystack. The more irrelevant text it receives, the worse the quality of the answer.

RAG solves this elegantly: it only retrieves the relevant fragments for each question. If someone asks about the return policy, the system only passes the paragraphs related to returns to the AI, not the entire product catalog.

What kind of information can RAG use

Any document that contains text is a candidate:

• Procedure manuals and internal documentation
• FAQs and knowledge bases for customer support
• Product catalogs with technical sheets and prices
• Contracts, regulations, and company policies
• Ticket or incident history resolved
• Relevant archived emails and communications
• Meeting minutes and documented decisions

You don't need the information to be in a special format. PDFs, Word documents, internal web pages, spreadsheets with descriptions—everything counts.

How it works internally

You don't need to be an engineer to understand the mechanism. There are five steps:

1. Ingestion: Chunking the documents

Your documents are divided into small, manageable fragments. A 50-page manual becomes hundreds of fragments, each only a few paragraphs long. Size matters: too large and it loses precision; too small and it loses context.

2. Embeddings: The semantic fingerprint

Each fragment is transformed into a numerical representation called an embedding. Think of it as a fingerprint that captures the meaning of the text, not the exact words. Two fragments that talk about the same topic will have similar fingerprints, even if they use different words.

3. Vector database: Storing for meaning-based search

These fingerprints are stored in a special database that allows searching by similarity of meaning. When someone asks "what is the deadline for returning a product," the system finds fragments about returns even if the document says "return policy" or "withdrawal period."

4. Query: Searching for relevance

When a question arrives, it is converted into an embedding, and the most similar fragments are searched for. The system retrieves the 3, 5, or 10 most relevant fragments, depending on the configuration.

5. Generation: Answering with real data

The AI receives the original question along with the retrieved fragments and generates an answer. The difference is that it now has real context from your company, so the answer is specific, accurate, and useful.

Practical Example: Technical support in an industrial company

Imagine a company that manufactures industrial machinery. Its field technicians need to consult maintenance manuals, failure histories, and technical sheets before every intervention. Without RAG, they search network folders, scattered PDFs, and ask colleagues by phone.

With an RAG system connected to their technical documentation:

1. The technician asks from their mobile: "What procedure do I follow for error E-47 on the XR-200 packaging line?"
2. The system searches the technical manuals, incident histories, and product sheets
3. It responds: "Error E-47 on the XR-200 indicates a failure in the pressure sensor of module 3. Procedure: 1) Check the connection of the PRS-47 sensor on the right side panel. 2) If the connection is correct, replace the sensor (reference SP-2847). 3) Recalibrate from the Configuration > Sensors > Automatic Calibration menu. In the last 3 similar incidents, the problem was a loose connection in 70% of cases."

The technician has the answer in seconds, with the exact procedure and historical data. Without calling anyone, without searching through folders.

RAG vs. fine-tuning: Why RAG is better for SMEs

When talking about customizing an AI, two paths appear:

Fine-tuning is like retraining the AI from scratch with your data. You change how the model "thinks." It is expensive, slow (days or weeks), requires advanced technical knowledge, and worst of all: it becomes obsolete as soon as you update a document. You would have to retrain every time.

RAG does not modify the AI. It only changes what information it consults. It is like putting a library next to it. If you update a document, you only have to re-index it, and the answers update instantly. No retraining, no waiting, no additional cost.

For the vast majority of SMEs, RAG is the correct option: cheaper, faster to implement, easier to maintain, and updateable in real time.

What is needed to implement it

You don't need complex infrastructure. The basic components are:

• Document Source: what you already have. PDFs, Word, internal web pages, databases. You don't need to create anything new.
• Embedding System: a model that converts text into numerical representations. There are free and open-source options.
• Vector Database: where the fragments are stored and searched. Accessible options like Chroma or Qdrant can work on a conventional computer.
• AI Model: the one that generates the answers. It can be Claude, a local model like Ollama or LM Studio, or any other compatible one.
• MCP Connection (optional): if you want everything to run locally and the data not to leave your company, you can combine RAG with an MCP server to maintain total control.

All of this can run on a local server or a private cloud. You don't need to hire large platforms.

Common mistakes to avoid

Indexing uncleaned documents

If your documents have repeated headers, footers with legal data on every page, duplicate indices, or poorly formatted text, the system will index them as is. Garbage in, garbage out. Basic cleaning before ingestion dramatically improves the quality of the answers.

Poorly sized fragments

Fragments that are too large include irrelevant information that confuses the AI. Fragments that are too small lose the necessary context. The optimal size depends on the type of document and requires some experimentation.

Not defining the scope

Not everything has to be in the RAG system. Defining which documents are relevant and which are not prevents noise and improves precision. It is better to start with a limited set and expand gradually.

Expecting perfection without iterating

The first version will give correct answers in most cases, but there will be questions that fail. This is normal. Reviewing the answers, adjusting the configuration, and improving the source documents is part of the process. A well-maintained RAG system improves over time.

Privacy and GDPR

One of the biggest advantages of RAG for Spanish SMEs is that it can run completely locally. Your documents are processed, indexed, and consulted without leaving your infrastructure. No data travels to external servers.

This is especially relevant if you handle:

• Personal customer data subject to GDPR
• Confidential commercial information
• Employee documentation
• Contracts and agreements with confidentiality clauses

Unlike uploading documents to cloud platforms like ChatGPT (where data can be used to train models), a local RAG system gives you total control over who accesses what information and where it is processed.

In 2026, with the European AI Act fully in force, this capability is not a technical luxury: it is a compliance necessity.

How we can help

At Navel Digital, we implement RAG systems adapted to the documentation and processes of each company. From cleaning and ingesting documents to the assistant running and answering with your real information.

We analyze which documents are a priority, configure the infrastructure (local or private cloud according to your needs), and leave you with a system that your team can use from day one.

If you want your company's AI to answer with real data instead of generalities, contact us without obligation.