Platform · Intelligence
NEV runs a learning loop. On-device agents learn from your life, a Hive of super-agents finds patterns across people facing what you face, and every insight is checked against what actually works for you before it reaches you.
How it works
Most apps look you up in a fixed table. NEV runs a converging loop that learns from outcomes, so the model of you keeps improving.
Green flows up: what your agents learn, de-identified, becomes signal for the Hive. Violet flows down: the Hive's hypotheses return to your phone and are kept only if they hold true for you.
The four stages
Agents on your phone learn from your meals, sleep, medications and biometrics. Nothing to log.
What they learn becomes de-identified signal that flows up to the Hive, without your personal narratives or exact health details.
Super-agents find patterns across thousands of people facing what you face, and form hypotheses worth testing.
Each idea comes back down and is checked against what actually works for you. Only what holds is kept.
A model of the individual
Underneath, NEV keeps a living model of you, not a lookup in a generic table. It updates every day as your body responds.
It figures out which foods and habits change how you feel and how your health markers trend, for you specifically.
The same meal can be a good idea for one person and a poor one for another. NEV answers for your body, not the average.
Every day of real-world results makes the picture of you a little more accurate.
Private by design
The intelligence layer lives on your phone. Anything that ever flows up is de-identified at the edge first, so the system can get smarter without anyone learning who you are.