How does Ingredo.app score a product?
Ingredo.app builds a transparent base score from established, source-cited data — Nutri-Score, NOVA processing, Eco-Score, and additive or allergen flags — then re-scores that same product against your own condition and diet profile, so the final verdict is personal, not generic.
The base score answers "is this generically good"; the personal verdict answers "is this good for me." Both are shown together, with the reasoning behind each visible, never a bare number.
Nothing in the pipeline is guessed: unknown ingredients are marked "insufficient data" rather than scored, and every flag traces back to a cited source.
The base score
For food, we reuse the Nutri-Score (nutrition), NOVA (processing level), Eco-Score (environmental impact), and additive/allergen analysis already computed by Open Food Facts, rather than re-deriving them ourselves.
For cosmetics, the base score is built from Open Beauty Facts ingredient data plus our own cited tags — for example comedogenic ratings and the EU Annex III fragrance allergen list — for what the source doesn't already cover.
Where an ingredient's classification is proposed by an offline AI enrichment step rather than a verified source, the score is marked as AI-influenced and the contributing estimates are listed for you to inspect.
Your personal verdict
Your profile — the condition and diet modules you've turned on — is layered on top of the base score. Each active module reacts to specific ingredient tags: gluten sources for Celiac, high-FODMAP ingredients for IBS, Annex III allergens for fragrance allergy, and so on.
Each ingredient tag maps to one of three flags: avoid (a hard block, like gluten for Celiac), caution (a soft penalty, like FODMAPs for IBS), or good match. Hard-block flags are always set from verified, human-reviewed data — never from an unverified AI proposal.
Common questions
Yes. Methodology changes are versioned, and a product's score can change if its recipe, the underlying data, or our ruleset is updated.
No. Offline AI enrichment only proposes tags for ingredients with insufficient data, and those proposals never produce a hard-block on their own — they go through human review or a deterministic cross-check first.