Mr Pet Lover
Every dog has a story. We help you understand yours.
The Problem
Dog owners and enthusiasts struggle to identify breeds accurately. Most breed ID apps are unreliable toys. Breed-specific care information is scattered across unreliable blogs. Nobody combines instant visual identification with deep, trustworthy breed intelligence.
The Approach
Computer vision breed identification powered by Google Vision and TensorFlow, combined with the most comprehensive breed profiles on the internet. Point your phone at any dog — get the breed, temperament, health risks, care needs, and training tips instantly. AI-generated educational videos make breed knowledge accessible to every dog owner.
Status
In Development
Category
AI & Automation
Founded
2024
Role
Founder & Technical Architect
Market
Dog owners, prospective dog buyers, veterinary students, pet industry professionals
Team
Solo founder + AI pipeline
Tech Stack
Next.js 16, Supabase, Remotion, Google Vision, TensorFlow
Domain
mrpetlover.comDeep Dive
Mr Pet Lover started from a simple observation: dog owners constantly ask "what breed is that?" but existing tools give unreliable one-word answers with no context.
The platform combines three capabilities that don't exist together anywhere else:
Instant Breed Identification — Point your camera at any dog. The computer vision model identifies the breed (or mix) with confidence scoring, then immediately surfaces everything you need to know about that specific breed.
Deep Breed Profiles — Not Wikipedia summaries. Structured profiles covering temperament mapping, exercise requirements by age, common health conditions with early warning signs, grooming schedules, dietary considerations, and compatibility with children and other pets.
AI-Generated Education — Remotion-powered video content that transforms breed data into watchable, shareable educational videos. Each breed gets a visual guide that covers what text alone cannot convey — movement patterns, size comparisons, coat variations.
The technical architecture uses Next.js 16 with Supabase for the data layer, Google Vision API for primary breed detection, and TensorFlow models for secondary validation and confidence scoring.
Milestones