The Nibblio Today screen: a streak note, a focus card that says more iron, better absorbed, the next meal with Log this and Swap buttons, and a why it works note.
Today. One focus, the next meal, and why it was picked.
The Nibblio meals screen for one day of the week: logged breakfast and snack, and the next lunch with Log this and Swap buttons.
Meals. The day's plan; one tap logs a meal.
The Nibblio Plan screen: a why this plan note about low ferritin, and today's macros as four rings for calories, protein, carbs and fat.
Plan. Built around the lab results, and it says why.

The problem

Most nutrition apps count calories and stop there. People who get blood work done see numbers like ferritin or vitamin D, and nothing tells them what to eat about it. Logging every meal by hand is also the main reason people quit these apps in the first weeks.

What we built

  • A phone app for iOS and Android, in English and Serbian.
  • Lab report upload: take a photo or upload a PDF, and the app pulls out the biomarkers.
  • A weekly meal plan and a shopping list built around the person’s focus, for example iron.
  • Four ways to log a meal, each a few seconds: text, a usual meal, a photo, or voice.
  • Habit support: a forgiving streak, a daily next step, a weekly wrap and weekly goals.
  • A backend of thirteen services behind one gateway, with test and production environments and automatic deploys.

The AI part

AI does four jobs, and each one has a limit.

  • Lab reports. A model reads a photo or PDF of the report and returns the biomarkers as structured data, which the person confirms.
  • Meal logging. Text, photo and voice logs become meals with nutrients. Voice is turned into text on the phone; no recording leaves the device.
  • Insights. The cause-and-effect findings (“you have steadier energy on days you eat breakfast early”) come from plain statistics on the person’s own logs, with a check against false positives. The model only rewrites a finding into friendly words. It does not invent findings.
  • Plans. A model drafts the weekly plan from the person’s focus, goals and allergies, and the app says why each meal is there.

Before launch we wrote a data protection impact assessment for each sensitive feature (photo, voice, health import, cohorts) and signed a data processing agreement with the model provider.

Where it is now

Nibblio is in a closed beta on TestFlight and the Google Play test track. The public site at nibblio.ai has a waitlist. We do not publish user numbers until there are real ones to publish.

Why it is here

It is our own product, labelled as such. It shows the kind of consumer app we build: a phone app people use every day, a real backend, AI with limits, and the privacy work that health data needs. It is not a client reference, and we do not present it as one.