Nutrition tracking for biohackers
You already measure HRV, glucose, sleep stages, and VO2 max. The nutrition layer is usually the gap. Calorie counters miss everything interesting. Açaí fills the gap with 245 micronutrients per meal and weekly deficiency modeling that actually correlates with what your wearables are reading.
The wearable is telling you something. So is the plate.
Most nutrition apps can’t answer why your HRV tanked on Tuesday. Açaí can, because the data was on the plate three meals earlier. A few common patterns we surface:
Correlation, not causation. Açaí surfaces the pattern. You run the n=1 experiment.
Designed to sit next to your other instruments
Açaí writes nutrients to Apple Health, which means the same layer pulling your HRV, sleep, glucose, and recovery now has micronutrient intake as a correlated input. If you use Oura, Whoop, Levels, Eight Sleep, or anything that reads from Apple Health, the data is already downstream.
What you get is a full stack: the body output (HRV, glucose, recovery), the behavior input (nutrition, sleep timing, training load), and a model that lets you notice when the two disagree. That’s the definition of a useful biohacking tool.
No fasting guilt, no daily streaks designed for habit formation, no coach yelling at you. Just dense data and a clean interface that doesn’t waste your attention.
Add the missing layer.
Your wearables already measure the output. Açaí measures the input with 245 data points per meal.