Foodvisor’s mobile app uses deep learning to identify the type of food in a photo and estimate the weight of each item. The app calculates the distance between the plate and the phone using autofocus data and then computes the area of each item; users can manually correct information before logging. The company has generated 2 million app downloads and recently launched in the U.S., which it views as a market opportunity. With the new funding, Foodvisor plans to improve the app and hire 15 more people. The latest reported financing is a $4.5 million round led by Agrinnovation with participation from Leonis Investissement and various business angels. Foodvisor built a mobile app that automatically logs meals from a photo using deep learning to detect food types and estimate weight/volume. The app calculates nutrition facts (calories, proteins, carbs, fats, fibers), lets users set goals, log activities and monitor progress. It estimates plate-to-phone distance using camera autofocus, computes item areas and extrapolates volumes by food type, and allows manual corrections when recognition is wrong. Foodvisor launched its app in 2018 in France after more than two years of R&D and is available on iOS and Android in French, English, German and Spanish. The startup has attracted 1.8 million app downloads and offers a premium subscription at $5–$10 per month that includes additional analysis, diet plans and in-app chats with registered dietitians. The company says it has adjusted its product and enriched its database to better target the American market and has raised $1.5M (€1.4M) to date.

Total raised

$6M

Funding rounds

2

Latest round

Equity

Latest activity

Nov 2019