In October 2026, the California-based company Hoomanely brought to market the EverBowl AI feeder — an intelligent feeding station that tracks how dogs eat and drink in order to detect changes in their health at an earlier stage. The device measures food and water intake, eating speed, the sounds of chewing and swallowing, thermal indicators of the muzzle, and movements of the oral cavity, and then compares the data with the animal's individual baseline.
Over 18 months of beta testing involving more than 80 dogs, the system collected over five million multimodal data points. During the trials, it recorded deviations that were later linked to tick-borne fever, early dental damage, and Cushing's syndrome treatment monitoring. The artificial intelligence builds a personal health profile based on daily meals, and in the event of prolonged deviations it sends notifications to the owner through the app and generates long-term reports for veterinarians.
EverBowl is positioned not as a diagnostic device but as a tool that complements visits to the doctor: it provides a continuous picture of the animal's behavior that cannot be obtained during a short appointment at a clinic. The company's chief veterinary advisor, Petra Harms, notes that owners often notice problems only weeks or months after they begin.
The station has become the first module of a planned platform that will in the future add sensors for movement, rest, weight, and environmental conditions. The company plans to conduct formal veterinary studies to confirm the accuracy of the signals on a larger sample of dogs. According to CEO Sai Supriya Sharath, the approach is based on biology: daily feeding rituals are the most stable moment for collecting data without disrupting the animal's usual routine.
Unlike ordinary automatic feeders, which focus on the convenience of portioning, EverBowl turns the bowl into a sensor that records subtle changes in appetite and feeding mechanics that may indicate pain, fever, or metabolic disorders. This opens up a new layer of at-home monitoring, in which nutrition data become the basis for more informed decisions by owners and veterinarians.
