The most common complaint about AI calorie apps, by a distance, is that the numbers are wrong. It appears in one-star reviews and — more tellingly — inside five-star ones, phrased as "it works, you just have to tweak it a couple of times". That sentence is the real state of the technology.
Here is what drives the error, what it is reasonable to expect, and what to do about it. We are building one of these, and this page is deliberately the least flattering one on the site, because "we are accurate" is the claim every app in this category makes and none of them can support.
The error is almost entirely portion size
Once an app knows it is looking at 165 g of grilled salmon, converting that to calories is a database lookup with very little uncertainty. Nutrition tables are good. The question is where the 165 came from.
A photograph is two-dimensional. Volume has to be inferred from apparent area, from a reference object — usually the plate or the bowl — and from priors about what portions of that food typically look like. Every one of those inferences is a place for error, and they compound.
| Food | What the camera can see | Practical confidence |
|---|---|---|
| Packaged item, label visible | An exact product | Very high — this is a lookup, not an estimate |
| Whole foods with familiar shapes — an egg, a banana, a slice of bread | Item and count | High |
| Separated plate — protein, starch, vegetable, not touching | Boundaries and rough area | Reasonable, portion is the variable |
| Bowl food — rice bowls, poke, noodles | A surface, not a depth | Moderate, and biased by how full the bowl is |
| Mixed dishes — curries, stews, casseroles | One brown region | Low without correction |
| Anything cooked in oil or dressed with sauce | Nothing. Fat is invisible. | Low, and it skews low — the hidden calories are the ones that got left out |
What "wrong" means in practice
It matters what you are using the number for. Two people can get the same estimate and one of them is well served while the other is not:
- Watching a trend across a week. Errors that are inconsistent partly cancel out, and the direction of the week is usually right. A photo app is genuinely good at this.
- Hitting a protein target. Better than most people expect, because protein sources are visually distinctive and their density is stable.
- Precise daily totals for a fixed deficit. Weaker. Meal-level error is real, and if you need the daily figure to be tight you will be correcting most meals.
- Anything clinical. No. These apps are not measurement instruments and should not be treated as one.
How to get better numbers out of any of them
- Photograph before you eat, at a slight angle. Straight down hides depth; a slight angle gives the model something to work with on a bowl.
- Get the whole plate and something for scale. The plate rim is the reference object. Crop it out and you have removed the only ruler in the picture.
- Correct the first time, not the fifth. A good app learns your repeated meals. The correction you make in week one is the one that pays off for the rest of the year.
- Say what you cannot see. If there was oil in the pan, add it. This is the single largest invisible error and it takes one tap to fix.
- Photograph the leftovers too, if there are any. Half a plate returned is half the calories, and no app can know that unless you tell it.
What we are doing about it
Two things, neither of which is claiming a percentage.
First, the segmentation is shown. You watch the plate get cut apart and labelled before you see a number. If it decided your chicken was tofu, you find out immediately rather than discovering a strange total at the end of the day. Showing the work is what makes a number checkable, and a checkable number is worth more than a confident one.
Second, correction is a first-class control, not an edit mode. It lives on the result card, always visible, with fractional portions — a third, a half, two thirds — because "about two thirds of that bowl" is the most common real correction anyone makes and whole-serving pickers cannot express it.
What we will not do is put a number like "94% accurate" on a screenshot. That figure would be measured against our own test set, chosen by us, and it would tell you nothing about your kitchen.
Before the App Store
Get it before it is public — and tell us what to build.
Early access on TestFlight, and no charge during the beta.
Is an AI photo estimate better or worse than my own guess?
For most people, better — untrained portion estimation by eye is unreliable in a well-documented way, and it tends to underestimate. The app is also wrong, but it is wrong more consistently, and consistent error still shows a trend.
Why do two apps give me different numbers for the same photo?
Different portion models and different nutrition databases. The gap between two apps on one photograph is a decent rough guide to how uncertain that particular meal is.
Does correcting it actually improve future results?
It should, for your repeated meals — that is the main mechanism by which one of these gets better for you specifically. Ask any app you are considering whether corrections persist.
Is a food scale still more accurate?
Yes, unambiguously, and it always will be. The question is whether you will still be using it in March. An imperfect method you keep doing beats a precise one you abandon.