Improve mobile photos with AI scoring + iterative coaching loop

Everyday mobile photographers — an estimated 1.0B active users globally — often take hundreds of photos but lack quick, objective feedback that helps them improve beyond filters and presets. Hobbyists, social creators, and small-business owners waste time and potential income because they can't reliably diagnose compositional, lighting, or framing issues in-shot. You could build a mobile-first product that combines an AI aesthetic and technical scoring engine with an iterative coaching loop: instant on-device scores, concrete corrective cues (e.g., adjust exposure by X stops, shift subject to left third), guided retakes, short micro-lessons, and progress tracking tied to measurable score gains. The app would run basic inference locally for privacy and latency while offering optional cloud features—batch analysis, personalized curricula, and creator coaching marketplaces—behind a freemium paywall. This market is attractive now because it maps directly to a $10.0B addressable opportunity (1.0B users × $10 ARPU/year), benefits from strong secular trends (consumer AI imaging, creator-economy microlearning, mobile-first camera improvements), and earns a Market Score of 95/100 with a Revenue Potential of 80/100. Low technical barriers to entry for basic features and growing acceptance of AI-driven coaching make early adoption viable, especially via partnerships with OEMs, social platforms, or print services. To stand out in a medium-competition landscape you must focus on verifiable improvement (showing measurable aesthetic score lifts), human-anchored calibration of scores, privacy-forward on-device inference, and a clear path to monetization—while acknowledging hard challenges around subjective taste, dataset labeling, retention economics, and converting free users to the ~$10/year ARPU.