Key Features

Log a Meal by Taking a Picture

• Bitesnap recognizes foods in the picture and automatically calculates calories and nutrients.
• This reduces the time needed to log a typical meal from minutes to just seconds.
• Bitesnap uses deep learning to identify over 1,300 food and drink categories.

Explore Eating Patterns Visually

• Bitesnap’s visual feed allows users to get a better picture of their eating patterns over time. Images provide context that text simply can’t.
• Eating patterns are the key to maintaining a healthy weight and reducing the risk of chronic disease. Bitesnap’s feed is an ideal tool for identifying, tracking, and improving these patterns.

Personalization

• Bitesnap uses machine learning to customize itself for each user.
• The app visually learns the user’s habits over time. Meals similar to those eaten in the past can be recognized and logged in their entirety with just one tap.
• Smart predictions based on time and pairings allow users to add items without typing, even when they don’t have a picture.

We’re Just Getting Started

We have a lot planned for Bitesnap, but we wanted to get it into users’ hands as soon as we had the core technology working. We’re looking forward to integrating ideas from the community as we build the next phases.

Why We Built Bitesnap

Several years ago, Michal (one of our founders) wanted to lose some weight. He found that keeping a food journal was an effective weight loss method. He tried a popular food logging app a few times in the process, but could never stick to it because it was too much work.

Recent advances in deep learning have made it possible to reliably recognize objects with near human-level accuracy. We decided to apply these techniques to food logging to simplify the process and make it more engaging – solving the problems that made Michal give up in the first place.

Along the way, we spoke with dozens of people whose experience mirrored Michal's. They wanted to track their diet, but were unable to stick with existing options for the long term. Bitesnap is built for them.

Obesity is a significant problem facing society today. Studies suggest that it is responsible for 1 in 7 premature deaths and leads to at least $147 billion in medical costs each year in the U.S. alone. We hope that one day, Bitesnap will be a small part of the solution.

About the Company

Bitesnap is built by Bite AI

• Founded in April 2016.
• Based in New York and Cambridge, MA.
• Our goal is to use machine learning to help people improve their health by gaining insight into what they’re eating.

Vinay Anantharaman, Co-Founder

Vinay began his career at Adobe as a software engineer working on the Flash player focused on audio and video decoding. More recently, he founded BrandBacker to help brands reach bloggers, and worked on the API at Clarifai, a computer vision startup. He has a BS in Computer Science from Cal Poly, SLO.

Michal Wolski, Co-Founder

Michal graduated from Columbia University in 2014, where he focused on Computer Vision and Machine Learning. Before double majoring in Computer Science and Applied Math, he spent 2 years studying Graphic Design at Queens College. He was the first employee at Clarifai, where he worked on the data and API teams.

Keith Ito, Technical Adviser

Keith has over 10 years of experience as an engineer at places ranging from tiny startups to industry leaders like Google. He started and led the team that built Google Maps Navigation and developed much of the early software stack for Google Glass. Keith has a BS and MS in Computer Science from Stanford University.

Katie Leonard, MS, RD, Nutrition Adviser

Katie has worked with adults and children of all ages to enrich our relationship with food and to increase awareness of our place in the food system. She holds a Master's Degree in Nutrition Education from Teachers College, Columbia University.

---

Contact

Need more info or want to speak in person? Contact us at:
Email: info@bite.ai
Twitter: @getbitesnap