— 4 reading minutes
Almost every company we spoke to already uses AI in some way, often without a clear policy. Someone on the team posts a contract in a chat to summarize it, another person asks about an internal procedure and trusts the answer. When an operations director or CTO wants to take this seriously, two questions arise: will things be made up, and where will the data go?
A Retrieval-Augmented Generation (RAG) is the most common way to address both of these issues. Here we explain how it works, what it improves, and what its limitations are.
What problem does it solve?
If you allow your team to use general-purpose AI tools to solve everyday questions, you face two serious problems: privacy and hallucinations.
On one hand, by entering internal data into a public chat, you are feeding third-party models with your company’s know-how. On the other hand, Large Language Models (LLMs) such as GPT-4 or Gemini are designed by nature to sound convincing, not necessarily to tell the truth.
When a general model does not know something, it rarely admits its ignorance; it simply invents an answer with astonishing confidence. In a business environment, a well-written lie is infinitely more dangerous than an obvious mistake. To avoid AI hallucinations, we need to change the rules of the game.
What is RAG?
Before the model responds, the system searches your documentation for relevant sections and passes them along with the query. This allows the model to draft the response based on specific company text.
The analogy we often use is that of a newly hired employee. If you ask them about an internal procedure on their first day, they'll most likely improvise. If you give them access to the file and ask them to look up the answer in the manuals, they'll get it right much more often and will be able to tell you where they found it.
How to assemble a RAG in practice
- Prepare the documentation. Gather the sources (HR manuals, operating procedures, technical documentation, ISO standards, internal wikis ), clean them up, and divide them into fragments. This is the least visually appealing part and the one that most influences the outcome: if there are three versions of the same procedure, the system may cite the one that is no longer valid.
- Index by meaning. Each fragment becomes an embedding , a vector representing its meaning, and is stored in a database. We typically use PostgreSQL with pgvector, so the vectors are stored in a database controlled by the client, along with the rest of their information.
- Search. When someone asks a question, the search is done by meaning as well as by words. "How many days off do I get for moving?" finds the section on paid leave even though the question doesn't use those words.
- Generate with limited instructions. The model receives the question and the retrieved fragments with a clear instruction: answer only with that information, cite where it comes from, and say that you don't know if the answer is not there.
Around this lies traditional software engineering: permissions, document updates, testing, and monitoring. This is the work we do in our custom AI development projects, where the search and model are integrated with the company's existing systems.
What improves
A maintenance technician searching for a protocol among 500 PDFs finds the answer in seconds and sees which document it comes from ("Maintenance Manual 2024, page 42"). They can verify it instantly, and in terms of security or customer relations, that's what makes people trust the tool.
The number of hallucinations is significantly reduced because the model works with specific text. And the documentation remains with the company: the texts are stored in your database, and with company APIs, the model provider doesn't use them for training.
What RAG doesn't solve alone
At Softspring, we believe technology should serve people, not the other way around. We don't design systems to replace your support or operations teams, but to save them hours of frustrating searches and allow them to focus on higher-value tasks.
This vision of responsible engineering is not just a matter of principle; it's also a regulatory compliance requirement. With the introduction of regulations such as the EU AI Act, companies will need to demonstrate that their AI systems are auditable, are not black boxes, and keep humans in control (human-in-the-loop).
Enterprise RAG is the first step towards governable AI. We don't sell blind automation; we deliver certainty. We assess your context, structure your data, and integrate technology into your existing workflows with common sense and technical expertise.
Want to know if your company is ready to implement its own intelligent search engine?
Through our Technology Consulting service, we can assess the state of your documentation and design an AI architecture that solves real problems without compromising your security. Let's talk about your needs.
Human supervision and AI Act
We designed these systems so that the support or operations team spends less time searching, with the final decision resting with one person. In practice, this means that each answer includes its source, that a record is kept of what was asked and what was answered, and that someone is responsible for the documentation that feeds the system.
This also helps with the European Artificial Intelligence Regulation (AI Act). An internal search engine for documentation isn't usually considered a high-risk use, but certain obligations still apply, such as the transparency requirements of Article 50 , which mandate informing users that they are interacting with AI when it's not obvious. If you'd like to delve deeper, we cover this in our article on AI governance in companies .
Where to begin?
What usually works is choosing a specific case study: a department, a type of document, and a group of people who ask a lot of questions. You test it with their actual questions, measure how many answers are correct and include sources, and then decide whether it's worth expanding the system. If you'd like to get an idea of the cost beforehand, we explain it in our article on how much AI costs .
Tell us what documentation you need and who would use it. We'll tell you if a RAG system makes sense for your situation and how we would incorporate it into a custom AI development project.