From training to deployment — the engineering work of getting machine learning models into production.
Short version: EverBowl captures behavioral patterns automatically, and EverWiz explains them in plain language, giving pet parents earlier clarity, often well before any visible symptom shows up. This post covers why single events are misleading, why vets actually care about trends, and how we're building a pattern-first health
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, 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
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