Intro Devices in the field constantly send motion, temperature, and power data. In connected pet-tech products like ours, catching unusual patterns right on the device can prevent bigger problems, like a collar overheating or a tracker's battery draining too fast. Detecting anomalies locally means we can react in
How AI models are shrinking without losing their minds. Many of our real-time models, from detecting dog motion to analyzing eating patterns, run directly on embedded systems. At Hoomanely, we pair lightweight CNNs like MobileNet with quantization and pruning to get low-latency inference without giving up reliability. Processing data locally
Introduction We're building technology that helps us understand pets better, not just visually but through sound. Dogs communicate through subtle cues: the rhythm of a chew, a quiet gulp, a bark that changes tone when they're anxious. At Hoomanely, our smart bowls and monitoring systems use
Introduction In specialized fields, the goal is a smarter system, not just a bigger one. Scaling large language models gets a lot of attention, but real gains often come from fixing the backend, and in a Retrieval-Augmented Generation pipeline, that backend is largely chunking. At Hoomanely, we kept running into