The Health Graph: Compounding Data Moat
A proprietary, high‑resolution dataset linking dietary inputs → biometric signals → performance & economic outcomes. Every cycle improves prediction + personalization.
1. Inputs
Meal composition, timing, micronutrients, context.
Meal composition, timing, micronutrients, context.
2. Signals
Sleep, HRV, glucose, cognitive self‑report, workflow friction.
Sleep, HRV, glucose, cognitive self‑report, workflow friction.
3. Outcomes
Focus stability, shipping velocity, sick days avoided, cost deltas.
Focus stability, shipping velocity, sick days avoided, cost deltas.
4. Optimization
AI refines prescriptions & ordering logic—closed loop.
AI refines prescriptions & ordering logic—closed loop.
Defensibility Flywheel
- Higher resolution → better predictions
- Better predictions → higher adherence
- Higher adherence → richer longitudinal data
- Richer data → widening performance gap
Why Competitors Can’t Follow
- They monetize transactions; we monetize validated outcomes
- We own multi‑surface ingestion (chat + passively + logistics)
- Contextual dataset is non‑portable & compounding
- API-first architecture: future ecosystem leverage
Strategic Moat
This is not “food delivery with AI.” It’s the foundational dataset required for underwriting preventative economics. Any insurer or platform that wants proactive risk reduction will need this resolution of ground truth.