SkillSaige is a little unusual because we don’t charge based on usage.
If you get a seat for an employee or student, they can practice as much as they want. There are no surprise bills based on how many conversations they have or how many tokens they use.
That’s not especially common for an AI-powered product. More usage usually means more processing, and more processing usually means more cost.
So how do we make unlimited practice practical?
A big part of the answer is that we do not ask the AI to run the entire system.
We Give the LLM a Smaller Job
A lot of AI products put the language model at the center and ask it to figure out what should happen next.
We took a different approach.
At SkillSaige, the language model focuses primarily on human communication. The surrounding software handles the things conventional software already does well.
Rules stay rules. State lives outside the model. The training system determines what is valid and what happens next. The LLM handles the language needed to make that experience feel natural.
That distinction matters because the model does not need to reconstruct the entire training experience every time someone says something.
That Changes the Cost
When we moved SkillSaige from a much more model-driven conversational system to this approach, token usage for a training scene dropped by over 90%.
That’s a big part of why we can make repeated practice practical without treating every additional conversation as a premium feature.
It also gives us much more control over the training itself.
The AI can interpret what a learner says and respond naturally, but it does not get to decide what the exercise is, rewrite the rules, or take control of the system.
There Is a Much More Detailed Explanation
I am simplifying this quite a bit.
Sayre, our CEO, calls the broader approach Semiotic Processing. The basic idea is to treat the LLM as a processor of communication rather than asking it to simulate an entire software system.
If you are interested in how the architecture actually works, I would recommend going there next.
The short version is that we can afford to give learners a lot of AI-powered practice because we are very deliberate about what we ask the AI to do.
Sometimes getting more out of AI starts with asking it to do less.
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