1. Goal model
The system compiles a personalized target-demand model from the selected goal and focus context.
Methodology
How the current product thinks about regimen optimization.
Supplement AI is trying to answer one practical question: how close is the user's current regimen to the best personalized target regimen for the outcomes they care about? Scores and upgrades are optimization signals, not diagnoses and not guarantees.
What the product is optimizing
- Goal-relevant support coverage for the active focus state.
- Amounts that are actually credible for the modeled target range.
- Products that are strong enough to support the upgrade well.
- Risk burden, interaction burden, and avoidable redundancy.
- The right upgrade type for the current regimen, not just the right ingredient.
Important boundaries
- This is decision support, not diagnosis or medical care.
- Score increases suggest better alignment, not guaranteed outcomes.
- Uncertainty should reduce confidence, not disappear into marketing copy.
- Safety context can block an otherwise attractive upgrade.
Pipeline
The current five-step view
2. Current-regimen parsing
Active products are resolved into interventions, forms, current coverage, and usable dose truth.
3. Family and product fit
Eligible families and products are ranked with semantic fit, dose-aware ordering, and quality gating.
4. Upgrade routing
The product decides whether the useful upgrade is adding, adjusting dose, swapping products, or changing timing.
5. Regimen Optimizer packaging
The output becomes one optimizer page with score lanes, product choices, and upgrade details.
Deep Dives
Read the research standards
How Supplement AI Evaluates Evidence
How research outcomes, similar populations, uncertainty, and regimen context become upgrades.
Open article
How Supplement AI Generates Evidence Ratings
How paper-level research quality ratings are generated from methodological rigor and reliability.
Open article
How Supplement AI Selects Products for Optimizer Upgrades
How label fit, quality importance, suggested serving, profile filters, and value shape product choices.
Open article
Understanding SupplementAI Product Quality
How product quality combines evidence, brand trust, and formulation integrity.
Open article
Risk of Bias in Dietary Supplementation Research
Why study design and hidden bias can change how much weight a result deserves.
Open article
Conflicts of Interest in Dietary Supplementation Research
How financial ties can shape interpretation and why disclosure matters.
Open article
Supplements Kill
A blunt look at why supplement safety cannot be treated like an afterthought.
Open article
Start from the product surfaces
The current story is strongest when you read it from the actual surfaces outward: the goal fit, the score lanes, the upgrade type, the product rails, and the follow-through path. Everything else is there to explain why those surfaces look the way they do.