AI
When engineers talk about Retrieval-Augmented Generation (RAG), the conversation often starts at embeddings, chunking, or vector databases. But the uncomfortable truth is this: RAG quality is bottlenecked by OCR quality. If the extracted text is noisy, fragmented, or wrongly segmented, no embedding model - no matter how powerful - can
AI
Detecting when something "sounds wrong" during a pet's mealtime seems simple - until you try to engineer it. Background noise, human speech, utensils clinking, bowls moving, fans humming, and wildly different pet eating styles create a dense acoustic jungle. Within this chaos, the goal is to
AI
Introduction: Why We Needed a Question Classifier At Hoomanely, we receive thousands of pet‑related questions across health, behaviour, grooming, food, training and more. These queries help us understand what pet parents care about-and where our product needs to improve. But manually reviewing every question is slow, inconsistent, and impossible
AI
Introduction In pet‑tech, the difference between “working” and “truly reliable” often comes down to how well your hardware understands the real world. At Hoomanely, we’ve spent months refining EverBowl’s proximity pipeline – the system that helps our smart bowl understand a dog’s face position, maintain safe distances,
AI
Introduction Modern applications-from image similarity to RAG systems-run on fast vector search. When you embed text, audio, or images into high‑dimensional vectors, finding the closest matches becomes a problem of approximate nearest neighbor (ANN) search. This is where vector databases like FAISS, Milvus, Pinecone, Weaviate, and Elasticsearch step in.
AI
Introduction Dogs communicate a lot-just not in words. How they tilt their head, shake their neck, scratch an itch, drink water, or suddenly sprint toward the door often carries more health and behavioral information than their bark. At Hoomanely, we’ve always believed that the next leap in pet health
AI
Introduction Image similarity search is one of the most versatile and widely used techniques in modern computer vision. Whether you're identifying duplicate images, powering search engines, detecting tampering, or performing lightweight recognition tasks on edge devices, the idea remains the same: convert images into numerical representations and measure
AI
Introduction Large language models are only as good as the information they can retrieve. As enterprise knowledge bases grow into millions of documents, traditional Retrieval‑Augmented Generation (RAG) systems begin to crack - not because the models are weak, but because the retrieval layer is too shallow. A single‑level
AI
Introduction At Hoomanely, we capture audio only when the proximity sensor detects a dog near the bowl. This ensures the sound we record is directly tied to feeding or drinking activity - not hours of irrelevant background noise. But even during those few seconds, real homes are chaotic acoustic environments:
AI
Introduction At Hoomanely, our mission is simple: give pet parents superpowers. From monitoring drinking patterns to spotting early temperature anomalies, our systems turn raw sensor data into clear, actionable health insights. But behind every insight is a critical intelligence layer - our rule‑based decision system. It bridges the gap