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# AI in Practice

What AI integration actually means once it has to ship and keep working.

What does "AI integration" actually mean in practice?

A model doing one defined job inside your product, where it measurably beats the alternative — sorting incoming messages, drafting text a person then approves, making search understand what someone meant rather than what they typed. It is not a chat bubble glued onto a finished website. The useful question is never "can we add AI" but "which repeated task is currently costing you time".

Where does AI already do real work in your own products?

In content. Our multilingual material is produced and kept current with model assistance and then reviewed per language before it goes public — never published straight from a model. That is also why we are careful when we talk about AI: we know from our own work exactly where the output is strong and where it still needs a person.

What happens to our data if an AI feature is involved?

Before anything is built we name which provider processes which data and what is retained, in writing. If that answer is not acceptable to you, the feature is designed differently or not built at all. No data leaves your system in a way you have not agreed to — and "we did not know it was being sent" is not an outcome we are willing to produce.