The key difference is accuracy. It is what makes a carb estimate truly useful. Carbetic combines AI with mathematics and real measurement data, making the app considerably more accurate than ordinary AI tools or calorie counter apps.
A general-purpose AI chatbot usually estimates a meal from a single photo. Carbetic measures it. Examples of why Carbetic is more accurate:
3D reconstruction instead of a single photo. Carbetic builds a 3D model of the meal from three captures. This yields the dimensions and volume of each component. Volume times density gives the weight, and the app calculates the nutrition values from that.
Vetted nutrition sources. The Carbetic database combines several reference databases: FoodData Central from the U.S. Department of Agriculture (USDA), manufacturers' nutrition data, official websites and other vetted sources. For each food, Carbetic uses the most suitable source: average values for generic items such as a burger bun, and the chain's official data when available.
Smartphone sensors. On devices with a LiDAR depth sensor, Carbetic includes its measurements when they improve accuracy. The gyroscope records the device's orientation and tilt for every capture. Carbetic also derives a coarse location, such as the country, from the IP address, because fast-food menus and recipes vary from country to country.
On top of that come an interface built for logging meals quickly rather than a chat thread, plus recipe analysis and voice input.
Source: U.S. Department of Agriculture, Agricultural Research Service. FoodData Central, 2019. fdc.nal.usda.gov