
SNAQ helps people with diabetes make smarter mealtime decisions by turning meal photos into quantified nutrition data. It uses patented photo-based food analysis powered by machine learning and computer vision to recognize items, estimate portion sizes, and link results to a nutrition database for calories, carbohydrates, and protein. The platform is a B2C digital health app with tools for healthcare professionals and has peer-reviewed accuracy demonstrated in a randomized controlled trial showing improved glucose Time-in-Range. SNAQ is used by hundreds of thousands of users and is positioned for clinical and consumer nutrition use.

SNAQ helps people with diabetes make smarter mealtime decisions by turning meal photos into quantified nutrition data. It uses patented photo-based food analysis powered by machine learning and computer vision to recognize items, estimate portion sizes, and link results to a nutrition database for calories, carbohydrates, and protein. The platform is a B2C digital health app with tools for healthcare professionals and has peer-reviewed accuracy demonstrated in a randomized controlled trial showing improved glucose Time-in-Range. SNAQ is used by hundreds of thousands of users and is positioned for clinical and consumer nutrition use.
What they do: Photo-based food recognition and portion-size estimation to produce nutrition data (calories, carbs, protein)
Primary users: People with diabetes (clinical & consumer nutrition use)
Founded: 2017, Zurich (SNAQ AG)
Funding: One disclosed grant round (Venture Kick, Dec 12, 2017)
Scale: Hundreds of thousands of users (reported)
Medical/consumer nutrition for diabetes management using image-based food analysis
2017
Data and Analytics
10085