I Built an AI That Reads Your Pet's Behavior — Here's How the Stack Works

Your dog stares at the wall at 3 AM. Your cat knocks everything off the table — again. Your rabbit is thumping for the fifth time today. You know something is off, but you can't decode it. I got tired of Googling "why does my dog spin in circles before lying down" and built something better: an AI that interprets pet behavior using a combination of NLP, a fine-tuned symptom classifier, and a lightweight RAG pipeline backed by veterinary literature. Here's exactly how it works under the hood. The Problem With Pet Behavior Data Most pet advice online is: Generic ("consult a vet" — thanks, Captain Obvious) Anecdotal (Reddit posts from 2011) Buried in 3,000-word listicles What pet owners actually need is fast, contextual interpretation — "My 4-year-old Labrador is licking his paws obsessively after walks" should yield a precise, ranked list of possible causes, not a Wikipedia entry on dogs. The Architecture 1. Input Layer — Free-text behavior description Users describe their pet's behavior