Introduction A language model is only as good as what it can retrieve. As knowledge bases grow into millions of documents, standard Retrieval-Augmented Generation starts to crack, not because the models are weak, but because the retrieval layer is too shallow. A single-level vector search often pulls back chunks that
Introduction At Hoomanely, we only record audio when the proximity sensor detects a dog near the bowl, so the sound we capture is tied directly to feeding or drinking rather than hours of unrelated background noise. Even in those few seconds, though, real homes are chaotic: TVs, conversation, clanking bowls,
Introduction At Hoomanely, the goal is straightforward: give pet parents better tools for spotting problems early. From drinking patterns to early temperature anomalies, our systems turn raw sensor data into clear, usable health insights. Behind every one of those insights sits a decision layer we call the rule-based engine, which
Introduction When you look at something, your brain merges two slightly different images from your eyes to work out depth and distance without any real effort. Getting a machine to do the same thing is a lot harder. At Hoomanely, stereo vision is central to how we read depth, shape,
Vision that starts with a square At Hoomanely, we're building systems that see and understand pets, from their eyes and movement to the bowls they eat from. That means our camera pipeline needs to detect key visual cues with precision and speed, even on a small edge chip
Intro You spend weeks training a model, then realize deploying it efficiently across devices, from cloud GPUs down to a Raspberry Pi, is a whole separate problem. That's where ONNX Runtime comes in. Built by Microsoft with a large open community behind it, ONNX Runtime (ORT) is the
Intro Look at a photograph and your brain instantly works out what's near and what's far, even though the image itself is flat. Machines don't have that instinct built in. Monocular depth estimation teaches a computer to infer depth from a single camera, no
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