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iOS app · Our own product

DietLense

DietLense is an iPhone app that turns a photo of your food into a nutrition breakdown, then helps plan meals, groceries and training around a goal. It is our own product, designed, built and published by Astel’s founder, and it is live on the App Store.

DietLense: screenshot of the live appDietLense: screenshot of the live app
Type
iOS app
Relationship
Our own product
Services
AI Solutions

The challenge

Calorie counting fails for most people because logging is tedious. Typing every ingredient into a database takes longer than eating the meal. The idea behind DietLense was to make logging take one photo.

Build a consumer app where AI does the tedious part, the numbers are grounded in real nutrition data, and the whole thing is solid enough to pass App Store review and be used every day.

What we built

A native iOS app built in SwiftUI, with accounts and sync, a subscription model, and a backend that analyzes meal photos and descriptions. Nutrition figures are grounded against USDA FoodData Central rather than left to the AI model alone, and the app links to its nutrition sources.

What makes it notable

The details that mattered.

Shipping AI to the public is different from demoing it. Results have to be consistent, costs have to be controlled per request, and health-related apps have to cite their sources to meet App Store guidelines. Building DietLense is where much of our practical AI experience comes from.

  • Photo-based meal logging that returns calories, macros and micronutrients
  • Barcode and manual logging alongside AI scans
  • Meal plans and grocery lists built around a chosen diet
  • Sign in with Apple and Google, with data synced between devices
  • Free and paid tiers managed through App Store subscriptions
  • In-app links to the nutrition sources behind its guidance
Explore the project

See it for yourself.

DietLense is available now on the App Store.

View on the App Store

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along these lines?

Tell us what you have in mind and how it differs from DietLense. We will explain what we would build and what it would take.

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