How accurate is Olivka?
Last updated: July 27, 2026 · Written and reviewed by the Olivka team — the people who build and evaluate the estimation pipeline described below.
Olivka is an AI calorie counter that lives in your chat apps: you send a photo, a voice note, or a short text describing your meal, and it replies with an estimate of the calories, protein, carbs, and fat. This page explains where those numbers come from, how large the error bars really are, and what you can do to make your log more accurate. The short version: no calorie count — from any app, label, or database — is exact. Olivka aims to be as accurate as the underlying food data allows, to be honest about uncertainty, and to make correcting an estimate a ten-second job.
Where the numbers come from
Olivka doesn't guess calories out of thin air. Every estimate is built from reference nutrition databases: USDA FoodData Central, the U.S. Department of Agriculture's public database of foods and their laboratory-measured nutrients, and Open Food Facts, the world's largest open database of packaged products — including tens of thousands of products sold in Poland and across Europe. For restaurant meals and branded items missing from both, Olivka can additionally search the web for published nutrition information. When you log "grilled chicken breast with rice", each ingredient is matched to a database entry, and your meal's calories, protein, carbohydrates and fat are calculated from those entries and the estimated amounts.
You can watch this work in every meal card: Olivka lists the ingredients it recognized and the totals it calculated, and the card has Confirm and Edit buttons. Nothing is logged as final until you've had the chance to correct it.
How a photo becomes a calorie count
When you send a photo, a vision AI model first describes what is actually on the plate: the dish, the visible ingredients, and an estimate of the portion sizes. That structured description is what the calorie calculation works from. Each recognized ingredient is matched against the nutrition databases above, and the portion estimates convert per-100-gram values into your meal's totals. Voice notes are transcribed first (Polish and English both work well), and plain text goes straight to the same matching step. The whole loop usually takes about ten seconds.
When a meal is ambiguous — a stew where the fat content could swing the total, a smoothie that might or might not have sugar — Olivka asks a short follow-up question instead of silently guessing.
What accuracy should you expect?
Calorie counting has an uncertainty floor that no app can remove. In the United States, printed nutrition labels are legally allowed to deviate from the true value by up to 20 percent, and European rules allow similar tolerances. Two reputable databases can list the same food with noticeably different values, because brands, recipes, and preparation differ. A tablespoon of oil that stays in the pan versus lands on your plate changes a meal by over 100 kcal. Any tool that promises exact calories from a photo is overpromising.
Within that floor, how you log matters more than which tool you use. Text with amounts ("200 g of cooked rice, one egg") is the most accurate — essentially database-level. A clear photo of a simple meal identifies the composition well; portion size is then the main source of error. Mixed dishes, soups, and sauces carry the widest error bars, because fat and sugar hide in them.
The practical rule: treat any single meal as an estimate, and base your decisions on daily and weekly totals logged the same way every day. Classic studies of hand-kept food diaries show people routinely under-report what they eat — a consistent, low-friction logger you actually use every day beats a precise one you abandon in week two.
How to get the most accurate counts
Add amounts when you know them: "200 g", "half the pack", "two slices". Weights beat photos; even rough amounts beat none.
Name what's invisible. Oil used for frying, butter on the pan, sugar or milk in your coffee, dressing on a salad — these are the biggest silent calorie sources, and no photo can see them. One extra word ("fried in oil") fixes it.
For packaged products, send the brand name or a photo of the label — packaged foods resolve to exact database entries, so they're the most precise thing you can log.
Photograph from above, with everything in the frame, before you start eating. A reference object like cutlery helps portion estimation.
Correct the card. If the estimate is off, tap Edit and say what's wrong ("that was 300 g, not 150") — the correction takes seconds. Your confirmed history also makes repeat logging faster: "my usual breakfast" resolves against meals you've already confirmed.
How we check Olivka's work
Accuracy isn't a set-and-forget feature. We routinely re-run samples of logged meals through an independent evaluation — comparing Olivka's estimates against reference models and the underlying databases — and investigate significant discrepancies. When a class of meals is systematically off, we fix the pipeline, not the individual answer. And because every estimate ships as an editable card, mistakes are visible and correctable by the person who knows the meal best: you.
Sources
Questions about how Olivka handles your data are answered in the Privacy Policy, and the FAQ on the home page covers pricing and supported apps. Or just try it: send Olivka one meal and check her work against this page.