Knowledge bases with AI are not a fad, nor just another tool in the technology stack. It is the answer to one of the biggest challenges facing companies today: finding the right information at the right time.

Imagine this: you are in the middle of a meeting, someone asks a question about an internal procedure, a specific customer or a report from last year. Silence. You start searching through emails, Drive folders, Slack messages... Ten minutes later, you're still the same. This, which seems anecdotal, is repeated daily in many companies. And it not only costs time: it slows down productivity, wears down teams and slows down key decisions.

The solution? A knowledge base with artificial intelligence in companies. And no, we're not talking about the future. This is already a reality, and many companies are gaining a competitive advantage because of it.

What exactly are AI knowledge bases?

It is not just a repository or a document manager. It is a system that unifies, structures and updates all the knowledge of a company -documents, manuals, policies, reports, FAQs, contracts, APIs- and makes it accessible through semantic queries. In other words, you can ask it as if it were a person: “What is our vacation policy?” or “What did we learn from project X with client Y?” and get direct, precise and up-to-date answers.

All this is achieved thanks to technologies such as vector databases and artificial intelligence architectures with agents that index content so that language models (LLM) can interpret it and generate natural responses. At Secture, we use RAG (Retrieval-Augmented Generation) architectures, which combine the best of both worlds: accurate search and generation of useful answers.

Why is this a game changer?

1. Immediate access to critical information You no longer need to know where the data is, only what you want to know.
2. Intelligent automation You can connect this base with a chatbot and automate customer support, onboarding, internal assistance, and even report generation or newsletters.
3. Saving time and resources What used to take days is now resolved in minutes. And that translates into money.
4. Adaptable to any size Freelancers, startups or large enterprises: everyone can benefit. And you don't need an army of engineers to get it up and running.

Challenges (yes, there are challenges too)

Not everything is magic. A powerful AI knowledge base needs planning:

• Data strategyWhat goes in, how it is organized and who has access.
• Quality indexingIf you enter poorly structured data, the answers will be of little use.
• Access controlnot everyone needs to see everything.
• Constant updatingcontent becomes obsolete quickly if it is not maintained.
• Continuous evaluationYou need to measure the quality of the responses and adjust.

Real (and useful) use cases

• Customer SupportA chatbot that responds 24/7 with your updated documentation.
• OnboardingNew employees have access to everything they need without overwhelming the veterans.
• Internal inquiries: any member of the team can consult policies, processes or manuals.
• Automatic analysis and summariesExtract insights from large volumes of data without breaking your head.
• Content generationfrom articles to newsletters, using your own information as a source.

How do I start?

Here is my recommendation:

1. Evaluate your objectivesWhat areas of your company would benefit the most? Where do you spend the most time looking for information?
2. Define your data strategyWhat to include, what not to include, and how to organize it.
3. Choose the right technologyYou can use commercial solutions or build a custom one (spoiler: at Secture we do it).
4. Form a minimum teamYou don't need five engineers. With a good developer, someone who understands your business and a well-connected LLM, you're good to go.
5. Itera and improvementas any living system, the base grows and improves with use and feedback.

And now, the important thing...

Companies that are already using these solutions are going faster. They are becoming more efficient. And they're making more informed decisions. If you don't start moving in this direction, you're likely to be left behind.

We have been developing systems of this type for clients for some time but they still won't let us talk about it, we would have to delete this post if we did because of the NDAs they make us sign. But we do have some interesting demos that we can show you if you are interested in seeing them.

We do it from scratch or on top of your existing systems, with customized solutions that don't cost a fortune or require years of development.

If you are interested in taking the leap, we speak. Because AI knowledge bases are not just a whim. techieare a real tool to grow better and faster.

And if after reading this you are curious and want to see how a knowledge base with RAG works in practice, don't miss out on this article from our blog, where we explain step by step how we developed our own AI agent for internal queries.

But if you prefer to see it live in action, we look forward to seeing you on the June 11, 2025 in Madrid (or if you prefer online) in our next RAG Architecture Workshop: Build Your AI Assistant. A perfect opportunity to learn, ask questions and take away applicable ideas from the first minute, with AI automation!

Do you prefer audiovisual content? Don't miss this video.