How estimates work
You do not need this page to use Kallo. But if you want to know where the number came from — and how much to trust it — here it is.
There are two things worth knowing, and both are things you can verify on your own screen.
The numbers come from data, not from the model's memory
The AI model does not invent the nutrition figures. It does reading comprehension: separating your sentence into dishes, then into ingredients, with the cooking method attached. The numbers come from food composition data — the Vietnam National Food Composition Table 2007, plus FAO and USDA data.
That is why chicken caesar salad comes back as four components, each with its
own row of numbers, rather than one figure for the whole plate.
When there isn't enough to go on, Kallo asks
The thing that decides most of the accuracy is not recognising the dish — it is the weight. "One bowl" could be 200ml or 700ml, and that gap carries through every number after it.
If you stated a weight, Kallo uses exactly that. If the dish is genuinely too ambiguous to settle, Kallo stops and asks which size you used, with reference photographs — rather than picking something in the middle and presenting it as though it were certain.
A useful estimate, not manufactured certainty.
Why text, not a photo
Kallo starts from the sentence you write. That is a deliberate choice, not a photo feature we haven't got to yet.
| Describing it in text | Taking a photo | |
|---|---|---|
| What you can see | Every ingredient, every number | One number for the whole plate |
| Fixing one detail | Correct that exact detail | Reshoot, re-guess |
| What a lens can't see | Oil, broth, sugar, what's buried | No way to know |
| Logging the same meal again | Same sentence, same result | Lighting and angle change it |
| Speed | You have to type | Faster |
That last row is the real cost, and we are not hiding it: typing a sentence is slower than holding up a camera. In exchange you get what a photograph cannot give you — you see what Kallo understood, you can correct the exact thing that is wrong, and logging the same meal tomorrow gives the same number.
Put another way, this is manual logging done properly: you still decide what you ate, you just don't have to translate it into ten separate searches.
What is never sent
The model receives only your meal description, your countries, your cooking habits and the output language. Your goal, targets, weight, age and sex stay on the server. Detail in Your data.
Read next
Accuracy and limits covers where this approach goes wrong, and by how much.