Food scoring shouldn't be a mystery number.
A score is only as good as its context. POWR uses dedicated methodology routes to judge whole foods, packaged items, and restaurant meals on their own terms.
Input
We ingest nutrition facts, ingredient lists, claims, and serving context.
Route
The item is routed to the correct policy (e.g. Whole Food vs. Packaged).
Score
Penalties and credits are applied based on the specific route's logic.
Explain
The final output explains *why* the score was given, not just the number.
Context Matters
Why one rule doesn't fit all
A banana and a protein bar might have similar carb counts, but they shouldn't be judged by the same rubric.
POWR's scoring engine separates Whole Foods from Packaged Foods so that processing signals, additives, and ingredient quality are weighed appropriately for the item type.
Whole foods get credit for nutrient density and lack of processing.
Packaged foods face strict scrutiny on additives, oils, and refined sugars.
Organic Banana
Generic Fit Brand
Chocolate Chip Protein Bar
The Policy Engine
We don't rely on a single hidden algorithm. The scoring system is built from explicit, tunable policy packages that you can understand.
Ingredients Policy
Looks for additive severity, seed oils, artificial dyes, high fructose corn syrup, and vague flavor systems.
Nutrient Policy
Evaluates sugar load, sodium density, fiber-to-carb ratios, and protein quality within context.
Processing Policy
Detects ultra-processed patterns: refined starch + fat combos, emulsifier stacking, and cosmetic additives.
Evidence Policy
Weights the confidence of the input. Verified label data beats OCR; OCR beats visual estimation.
Route Policy
Determines the active methodology. Prevents supplements from being scored as food, or water as a meal.
Comparison Policy
Scopes comparisons to relevant peers. "Best in class" means best in this category, not all food.
Reference
Methodology Definitions
Core Concept
A small rule surface for obvious whole foods with minimal processing.
When to use
Use this route when the item is a single recognizable whole food with no meaningful additive system or complex ingredient list.
Technical Focus
The docs describe this as the simplest scoring route: strong floors for obviously whole foods, limited penalties, and more emphasis on nutrient density, fiber, protein, and micronutrient contribution.
"This route is meant to avoid over-engineering simple foods. A banana should not be judged like a protein bar."
Core Concept
The strongest first deterministic route because labels and ingredients are more structured.
When to use
Use this route when there is a nutrition label, ingredient list, or barcode-backed product record.
Technical Focus
This package emphasizes ingredient quality, additive severity, processing patterns, sugar load, sodium load, protein and fiber density, oil quality, and label transparency. The architecture docs also define caps so protein or fiber credits cannot fully cancel heavy processing penalties.
"This is where ingredient policy, nutrient policy, and processing policy all become highly visible to the user."
Core Concept
A lower-confidence route built around incomplete evidence and preparation uncertainty.
When to use
Use this route when the item comes from a menu, chain nutrition facts, or AI inference without a complete packaged-food label.
Technical Focus
The docs call for wider uncertainty bands, more conservative assumptions, and fewer claims of precision. Likely deep-fried items, sauce-heavy dishes, and refined-carb-plus-fat combinations can be penalized, but confidence should often be lower than in packaged food.
"The point is not to fake exactness when the evidence is weak. Evidence policy matters as much as nutrition policy here."
Core Concept
A component-based route for meals assembled from multiple foods with partial certainty.
When to use
Use this route when the system can identify major components but exact oils, quantities, or prep methods remain fuzzy.
Technical Focus
The architecture suggests scoring major components separately, then aggregating them by estimated serving contribution. Confidence should depend on ingredient certainty and portion certainty rather than pretending every mixed meal is fully known.
"This route is designed to preserve useful guidance without overstating how precise a homemade meal score really is."
Core Concept
A separate methodology family so supplements are not flattened into food logic.
When to use
Use this route for powders, capsules, tablets, electrolyte mixes, gummies, and other supplement formats.
Technical Focus
The docs say supplements should be separated from food entirely. Scoring should emphasize additive load, sweeteners, sugar, excipients, and transparency, while treating very different supplement classes, like protein powders and vitamin gummies, with different internal rules.
"This is another example of route policy doing real work: a supplement score should not pretend to mean the same thing as a food score."
Core Concept
A versioned policy package that shows how subcategory-scoped scoring works outside food.
When to use
Use the `water.bottled_water.v1` methodology when scoring bottled water products against other bottled waters.
Technical Focus
The bottled water docs define explicit buckets for contaminants, materials, source quality, transparency, recalls, and certifications. PFAS, lead, arsenic, microplastics, and chlorate are example contaminant rules. Packaging material, recent lab reports, and evidence completeness shape both score and confidence.
"This is the clearest proof that scores need scope. Bottled water scores are intentionally not comparable to tap water, supplements, or food scores."