The challenge
What needed to be easier.
A food photo is easy to take, but turning it into useful nutritional information requires accurate recognition and an explanation people can understand quickly.
← Selected work
Computer vision product
Shazam for Food
We built a computer vision app that identifies food from a photo, estimates calories, and highlights key ingredients.
- AWS
- Node.js
- Python
- React Native
Updated 2026-09-06
A photo. Useful context.Product interface / workflow overview
Our approach
How we built it.
We combined a mobile capture flow, computer vision services, and calorie reference data to identify food and present a useful estimate.
Inside the experience
From a food photo to useful context.
A visual guide to the published product scope.
- 01
Capture
Take a photo in the mobile app.
- 02
Recognize
Computer vision identifies food items.
- 03
Estimate
Reference data informs a calorie estimate.
- 04
Understand
Present ingredients and nutritional context.
Conceptual illustration · Based on public capabilities, not a private architecture diagram.
Product capabilities
What we brought together.
We only include capabilities that are already public. We do not share client data, private architecture, commercial terms, or internal performance metrics.
Mobile image capture
Food-item recognition
Composition analysis
Calorie estimation
Ingredient labeling
Cloud inference workflow
Related case study
Document OCR
We built an in-house OCR workflow that extracts driver license data, reduces manual entry, and gives operations teams a review step.
