Overview / Products
Your products, your numbers
A personal reference list with label values per 100 g or ml and your standard portions. The AI matches what you log against it, so your regulars are exact, not estimated.
What you can do here
- Add products with label values per 100 g/ml, straight from the packaging.
- Define standard portions once (a scoop, a slice, a glass); the AI applies them.
- The AI matches food names to your products automatically when you log.
- New products can be created right from a photo or a chat message.
- Products can carry a vendor (Coop, Migros, homemade), so two similar yogurts stay apart.
- Edit or remove products anytime; past entries keep their values.
Label values beat estimates
Generic food databases are full of duplicates and wrong entries. KIkal prefers the values printed on your actual products. Only when nothing matches does the AI estimate, and then it tells you how confident it is.
1,207 foods come built in
Next to your own products, KIkal ships a reference catalog of everyday foods with official values per 100 g, so common food is grounded in real numbers from the first day, before you have added anything yourself.
- 1,207 generic foods (bread, cheese, meat, vegetables, prepared dishes) with calories and protein per 100 g.
- 4,828 names across German, French, Italian and English: log “Pouletbrust”, “poitrine de poulet”, “petto di pollo” or “chicken breast” and you land on the same food.
- Catalog foods are marked as such in your list, so you always see whether a number comes from the catalog or from a product you entered yourself.
- The catalog updates itself when a new version of the source is published.
The data comes from the Swiss Food Composition Database of the Federal Food Safety and Veterinary Office (FSVO), version 7.1. It describes food on the Swiss market, which is exactly what KIkal is built for, and it is the reason the numbers are trustworthy rather than crowd-sourced.
Nutrition values are not the same everywhere: recipes, fortification and portion conventions differ from country to country, so we deliberately do not mix markets into one pile. For now the catalog is Swiss. Data for other countries is planned, and it will be added as separate sources rather than blended in, so you can always tell which market a value describes. Until then, anything the catalog does not cover still works: you add it as your own product, or let the AI estimate it and say so.