Most calorie apps assume a Western plate.
Calorite doesn't.
Ask a typical AI photo-scan model to estimate a bowl of dal, and it will often strip out the ghee, guess a Western-sized portion, and price the dish in USDA terms alone — because that's the plate its training data was built around. Calorite classifies the cuisine first, then applies that cuisine's own conventions.
Every photo scan runs through a cuisine classification step before estimation begins. The model looks at the tableware, plating, and ingredients and assigns one of four buckets — then applies rules specific to that bucket, instead of one universal assumption.
South Asian
East Asian
Western
Everything else
USDA alone isn't built for every kitchen.
USDA FoodData Central is the default reference most US-built calorie apps rely on — it's comprehensive for American and Western food, but thin on South Asian home cooking. Calorite cross-checks against the Indian Food Composition Tables (IFCT) as well, so a dal or a dosa isn't approximated from the nearest Western analog.
Hidden-fat detection. Cooking oil and ghee are easy for a vision model to miss entirely, especially in a photo where the oil has mostly been absorbed into the dish. When the model sees a real risk of extra fat beyond what's typical — pooling oil, a heavy shine, visible ghee, a deep-fried surface — it flags it, and the app offers one-tap chips to add the specific oil or sauce yourself, rather than silently under-counting.
The Global Food Database
No fixed reference database covers every regional dish, home recipe, or regional packaged product. Calorite's Global Food Database lets users submit real dishes — moderated before they're searchable — so coverage keeps growing in the direction actual users are logging, not just where a corpus happened to be built.
Scan a meal that isn't a burger.
Start a free trial and try the photo scan on whatever you're actually eating.