Signals in. One living picture. Honest outputs.

Signals in

AXLE listens to the streams you connect, continuously and quietly. Your body: heart-rate variability, sleep, resting heart rate and workouts via Apple Health. Your training: every session you log or import, with sets, paces, loads, and personal records. Your life: calendar, travel, weather, and the equipment you actually have access to today.

One living context

Every signal lands in a single, versioned picture of you. One context, not a pile of disconnected charts. It holds your baselines, your training load, your preferences and your history, and it is rebuilt the moment something meaningful changes: a hard run this morning, a red-eye tonight, a new PR.

Baselines are yours, not borrowed. AXLE learns what normal looks like for you, then reads every new signal against it.

The day’s outputs

From that context, AXLE writes the day: a workout forged for the body you woke up with, a readiness read with its reasoning attached, a morning briefing in plain language, and, when it genuinely helps, a recommendation for something you’re about to run out of.

Nothing is pulled from a content library. Each output is generated against your context. That is why two users never see the same day.

The learning loop

Then AXLE watches what happens. Sessions you finish, sessions you skip, feedback you give, corrections you make: every one of them adjusts the picture. Tell it “never show me creatine again” and that becomes a standing rule, not a suggestion it forgets by Thursday.

The longer you use it, the more specifically it is yours.

Honesty principles

Learning systems guess. AXLE’s rule is that a guess must be labeled as one. Confidence travels with every output, and when the data is thin the engine narrows its claims instead of widening its adjectives.

Privacy architecture

The engine only works because it sees a lot of you. The handling has to be worthy of that.