When I talk about private AI to a business leader, I often see the same hesitation cross their face. Private compared to what? The phrase is worth pausing on, because it captures the exact difference between a conversational gadget and a serious working tool.
And to understand why it matters, we may as well start with a true story.
The day Samsung banned ChatGPT for its engineers
In spring 2023, a few weeks after allowing its teams to use ChatGPT, Samsung reversed course and banned it. The reason is simple and instructive: engineers had pasted internal source code and confidential meeting notes into the tool, to have them fixed or summarised. That sensitive information had left for external servers, beyond the company’s control, and potentially reusable.
No one at Samsung had acted out of malice. The engineers were doing what the tool invites you to do: give it content so it can help. The problem was not them. It was the nature of the tool. A public tool, built for the general public, with everything that implies about what becomes of what you hand it.
That story sums up the whole stake. The question is not only whether the AI answers well. It is also where the data goes when you use it.
Public versus private: the real dividing line
A consumer AI, the kind everyone uses in their browser, answers from what it has learned of the web in general. It knows nothing of your company, and depending on its terms of use, what you send it may be kept or used to improve it. Handy for general questions. Risky, even pointless, the moment it touches your own reality.
A private AI works the other way around. It answers from your documents, your procedures, your data, and no one else’s. What you entrust to it stays with you. It knows your catalogue, your standard contracts, your methods, and it will not spill them elsewhere.
The consequence is twofold, and decisive. On one side, answers become relevant, because they speak of your house and not of some blurry average of the internet. On the other, they become verifiable, because a well built private AI shows which internal document it relies on. You can trace back to the source in one click, instead of taking its word for it.
Sourced, or nothing
I press on this matter of the source, because it is what separates the serious from the decorative. An AI that answers without saying where it got its information asks you for an act of faith. In business, an act of faith has no place on a quote, a customer reply or an internal memo.
A private AI worthy of the name does not guess, it retrieves. You ask a question, it searches your content for the relevant passages, builds its answer from them, and shows you which ones. And if the information does not exist in your house, it says so, instead of filling the gap with something plausible. That honesty, about what it does not know, is worth more than a thousand brilliant, uncontrollable answers.
This is exactly the principle I design Ablyos on: a layer of intelligence laid over what already exists, gathering documents, processes and internal resources into a single, strictly sourced reference, usable inside and outside the company. Not one more assistant talking into the void. A company memory that answers, and shows its proof.
What it actually solves in an SME
There remains the question that matters to a leader: what is it for, concretely?
In many small companies, knowledge lives in people’s heads and in files no one can find. Someone leaves, and part of the knowledge leaves with them. A procedure exists, but it sleeps in a file no one opens any more. The new hire takes three months to learn what the old hand knew by heart.
A private AI tackles this head on. It turns a stock of inert documents into a living memory, searchable in plain language, and reliable because traceable. The salesperson finds a clause in a standard contract in ten seconds. The newcomer asks their questions of the company itself instead of bothering three colleagues. The leader stops being the bottleneck through which all information passes.
There is a caveat to keep in mind, to stay honest: a private AI is only as good as what you feed it. Plugged into outdated or messy documents, it will answer with just as much assurance, but on false ground. Setting it up therefore forces you to tidy your knowledge a little, and that is often the first value of the project, before the tool itself.
At bottom, the difference between playing with AI and making it a tool sits right there. Public AI lends you a general intelligence, at the price of your data and without knowing your trade. Private AI puts your own knowledge to work, in your house, and lets you check. For a company, that is not a technical nuance. It is the difference between a curiosity and an asset.