We built an AI to analyze 100 biomarkers, then realized it has no intuition
We spent the last year building an AI to analyze 100+ biomarkers (metabolism, toxins, organ function) and perform predictive analysis on disease risks. The goal was to build a "Whole-Body Intelligence System".
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The tech worked, but the solution was incomplete. We realized that while AI is excellent at functional grading across 10 health domains , it lacks "latent knowledge"—the intuition a doctor gains from 50 years of clinical practice that isn't in textbooks or datasets.
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So, we pivoted from a pure-AI SaaS to a hybrid model: AI processes the data to find the signal, and a dedicated "genius doctor" interprets the "soul" of the data to create the protocol.
We are trying to solve the "last mile" problem in preventive health: turning raw data into reversed chronic conditions
The tech worked, but the solution was incomplete. We realized that while AI is excellent at functional grading across 10 health domains , it lacks "latent knowledge"—the intuition a doctor gains from 50 years of clinical practice that isn't in textbooks or datasets. +1
So, we pivoted from a pure-AI SaaS to a hybrid model: AI processes the data to find the signal, and a dedicated "genius doctor" interprets the "soul" of the data to create the protocol.
We are trying to solve the "last mile" problem in preventive health: turning raw data into reversed chronic conditions
http://nostaviahealth.com