TTcare

Analyzes uploaded pet photographs using convolutional neural networks to detect visible health indicators (skin conditions, eye discharge, coat quality, body condition scoring) and generates preliminary health assessments. The system processes image metadata alongside visual features to contextualize findings within breed and age parameters, producing confidence-scored health concern flags that are ranked by severity for user presentation.

Maintains a time-series database of pet health assessments from uploaded images, enabling longitudinal comparison of visible health indicators across weeks or months. The system detects changes in detected conditions (e.g., skin lesion progression, coat deterioration, eye discharge intensity) by comparing current image embeddings against historical baselines, surfacing trends that may warrant veterinary attention.

Incorporates pet breed, age, and demographic metadata into health assessment logic to adjust baseline expectations and risk factors. The system applies breed-specific health predispositions (e.g., hip dysplasia in large breeds, brachycephalic breathing issues) and age-appropriate concern prioritization (e.g., dental disease in senior pets) to generate personalized health flags rather than generic assessments.

Classifies detected health concerns into severity tiers (monitor at home, schedule routine vet visit, seek urgent care, emergency) based on condition type, confidence score, and pet context. The system generates actionable recommendations with urgency messaging, enabling pet owners to make informed decisions about veterinary care timing without clinical training.

Implements a freemium pricing model with limited free assessments (e.g., 2-3 per month) and premium subscription unlocking unlimited assessments, trend tracking, and advanced features. The system tracks usage metrics, presents upgrade prompts at feature boundaries, and manages subscription state to control feature access.

Maintains user accounts with encrypted storage of pet profiles, assessment history, and uploaded images. The system implements authentication (email/password or social login), data encryption at rest, and access controls to ensure privacy of sensitive pet health information.

Converts structured health assessment data (detected conditions, confidence scores, severity flags) into human-readable natural language summaries explaining findings in accessible language. The system generates personalized explanations that contextualize findings for the specific pet and provide actionable next steps.