Not everyone has type 2 diabetes, the disease is the cause of chronically high blood sugar levels, but many do. About 9% of Americans suffer, and another 30% are at risk of developing it.
In January, put software by AI, a four-year-old, subscription-based startup that began offering personalized nutritional and activity-related suggestions to its customers based on a combination of food-related data, in November, the company launched three Silently disturbed the year, and the unique profile of each person, which shines in the first four days of using that person’s software.
Why the need for personalization? Because believe it or not, people can react very differently to every single meal, from rice to salad dressing.
Tech Sound may be mundane, but it is eye-opening and potentially live-saving, with cofounder and CEO Noshen Hashmi and his cofounder, Michael Snyder, a genetics professor at Stanford, who focuses on diabetes and pre-diabetes , Promises over the years.
Investors also like the idea. Felicitas Ventures led a $ 21 million Series A investment in the company, which included Hand Capital and Salesforce founder Mark Benioff. (Earlier investors include Jerry Yang’s Aime Cloud Ventures, Signalfire, YouTube cofounder Steve Chen, and Sunshine cofounder Marissa Mayer.) While Fellisis founder Aydin Senkut says, “While other companies have used biometric sensor data to understand heart rates Made headway from. Glucose monitor, for example- January AI has progressed in analysis and Prediction Effects of food consumption [which is] It is important to address chronic disease. “
To find out more, we spoke to Hasami and Snyder this afternoon. Below is part of our chat, which has been edited for length and clarity.
TC: What have you made?
NH: We have created a multimedia platform where we take data from various sources and estimate people’s glycemic response, allowing them to consider their choice before selecting them. We draw data from heart rate monitors and continuous glucose monitors and a 1,000-person clinical study and atlas of 16 million foods, for which, using machine learning, we have derived nutritional values and created nutrition labeling. [that didn’t exist previously].
[The idea is to] Prediction for [customers] What would be their glycemic response to any food in their database after just four days of training. They don’t really have to eat food to know whether they should eat it or not; Our product tells them what their reaction is going to be.
TC: So glucose monitoring already existed, but it is predictive. why is it important?
NH: We want to bring happiness back to food and remove crime. We can, for example, estimate how long you must walk after eating any food in our database to keep your blood sugar at the right level. Knowing “what” is not enough; We want to tell you about it. If you are thinking of fried chicken and shake, we can tell you: to maintain healthy you have to walk after 46 minutes [blood sugar] Limit. Would you like uptime for this? No? then maybe [eat the chicken and shake] on a Saturday.
TC: This is subscription software that works with other wearables and is priced at $ 488 for three months.
NH: That’s the retail price, but we have an introductory offer of $ 288.
TC: Are you worried that people will use the product, get information about what they can do differently, then unsubscribe?
NH: No. Changes in pregnancy [one’s profile], Age changes it. People travel and they do not always eat the same things. . .
MS: I am wearing [continuous glucose monitoring] Weaving for seven years and I still learn stuff. You suddenly realize that every time you eat white rice, you spike through the roof, for example. This is true for many people. But we are also offering a one-year membership soon because we know that people sometimes slip [only to be reminded] Later these boosters are very valuable.
TC: How does it work practically? Say that I’m in a restaurant and I’m in the mood for pizza, but I don’t know who to order.
NH: You can compare curve to curve to see which is healthiest. You can see how much you have to walk [depending on the toppings].
TC: Do I need to speak all these toppings in my smart phone?
NH: Jan scans the barcode, it also interprets the photos. It also has a manual entry, and it takes voice [commands].
TC: Are you doing anything else with this large-scale food database that you have collected and you are enriching with your own data?
NH: We definitely won’t sell personal information.
TC: Not even collected data? Because it does sound like a useful database. . .
MS: We are not 23andMe; This is not really the goal.
TC: You mentioned that rice can cause someone’s blood sugar to be soaked, which is amazing. What are the things that can make people wonder what your software can show them?
NH: The way the glycemic response of people is so different, not only between Connie and Mike, but also for Connie and Connie. If you eat nine days in a row, your glycemic response may vary in each of those nine days because of how many days you slept or how much you thought or how much fiber your body had and whether you slept. Had eaten before.
The activity before eating and after eating is important. Fiber is important. It is the most commonly overlooked intervention in the American diet. Our parental diet consists of 150 grams of fiber a day; The average American diet today contains 15 grams of fiber. Many health problems can be detected by lack of fiber.
TC: Looks like coaching will be helpful in concert with your app. Is there a coaching component?
NH: We do not offer a coaching component today, but we are in talks with several coaching solutions as we speak, for them to be an AI partner.
TC: Who else are you partnering with? Healthcare Companies? Employers who can offer it as a benefit?
NH: We are selling to direct consumers, but we already have a pharma customer for two years. Pharma companies are very interested in working with us because we are able to use the lifestyle as a biomarker. We essentially give them [anonymized] Visibility in one’s lifestyle for a period of two weeks or however long they want to run the program, so that they can gain insight into whether the medical person is working because of their lifestyle or regardless of a person’s lifestyle Used to be. Pharma companies are very interested in working with us because they can get answers at a testing stage potentially faster and even reduce the number of subjects they need.
So we are excited about pharma. We are also interested in working with employers, with coaching solutions, and finally, with payers [like insurance companies].