AI
Audio classification on edge devices presents a unique engineering challenge that doesn't get enough attention in the ML community. While everyone's building massive transformer models that need GPU clusters, we needed something that could run on a Raspberry Pi and accurately detect different types of dog
AI
The "Cold Start" Problem in Audio AI In the lifecycle of any machine learning project, the most painful phase is often the very first one: Data Curation. Before you can train a model to detect a dog bark, you need thousands of labeled examples of dog barks. But
AI
Edge AI is transforming how devices interact with the real world, from smart speakers to pet care devices. A critical challenge in deploying these models is robustness in varied acoustic environments. While collecting massive labeled datasets can help, real-world constraints often limit data availability, especially for specialised domains like pet
AI
When your edge device captures thousands of audio clips in real-world conditions, the question isn't just "what's happening?" — it's "what's going wrong?" At Hoomanely, our Everbowl continuously monitors a pet to provide preventive healthcare insights for pets. But
AI
Machine learning promises automation at scale, but building reliable audio classification systems reveals a persistent challenge: ground truth validation. When we set out to build an edge-deployed dog sound classifier, we explored every path to automated labeling—and each one led back to the same conclusion: human perception isn'
AI
When your AI model performs flawlessly in testing but stumbles in production, you're likely facing one of computer vision's most persistent challenges: domain shift. At Hoomanely, we encountered this exact problem with our dog segmentation model deployed on the Everbowl, our edge device designed to monitor
Software
At Hoomanely, we're building a preventive pet healthcare platform for pet parents that has tools and features ranging from pet-parents community to a AI powered pet care chat assistant. Our mission demands rapid iteration—we need to test hypotheses quickly, deploy features fast, and scale efficiently without the
Software
Ask any pet parent about their anxious dog, and they'll give you a novel. Ask an AI assistant the same question without proper context, and you'll get generic advice that could apply to any animal, anywhere. Here's what we learned building Hoomanely's
Software
Imagine this: You're chatting with an AI vet about your anxious rescue dog, Luna. Over hours, you've discussed her separation anxiety, dietary restrictions, medication schedules, and behavioural patterns. Then the next day, you ask a follow-up question about her progress—and the AI responds as if
Software
Onboarding friction kills conversion. When building Hoomanely's pet parent onboarding flow, we discovered that one seemingly simple question - "What breed is your pet?"—can cause unexpected dropoff. Pet parents would pause, scroll through endless dropdown menus, type partial breed names that didn't match