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Business Plan: AI-Powered Functional Fitness Coach 1. Executive Summary The proposed application is an AI-powered virtual personal trainer designed specifically for kettlebell and functional fitness enthusiasts. Utilizing advanced computer vision via the user's smartphone camera, the app tracks complex, dynamic movements in real-time. By bridging the gap between expensive in-person coaching ($60-$120/session) and generic fitness apps, this platform offers professional-grade form correction for a highly accessible $14.99/month subscription. Vision & Quantified User Impact Our core vision is to democratize expert-level coaching and reduce home-based kettlebell and functional fitness injuries by 40%. This target is initially grounded in existing biomechanical research demonstrating the efficacy of real-time auditory and visual feedback in movement correction. To rigorously validate this metric, we are launching a 90-day internal study with a targeted beta cohort of 200 active TestFlight users, monitored closely by our partner RKC-certified instructors to track technique improvements and incident rates. Post-launch, ongoing validation will be conducted through continuous user self-reporting and wearable health metric tracking. By providing validated real-time biomechanical feedback, we empower users to train safely, confidently, and effectively without needing an in-person trainer. 2. Problem & Solution The Problem: Free weights, particularly kettlebells, require highly technical form. Incorrect execution of dynamic movements like swings, cleans, and snatches can lead to severe injury. Enthusiasts want to perfect their form but cannot always afford or access specialized coaches. Looking at a screen during these explosive movements is impractical and dangerous. The Solution: A mobile application that acts as an expert set of eyes. The app uses the phone's camera to monitor the user's skeletal alignment and movement velocity, delivering real-time audio corrections during the set to ensure safety, followed by a detailed visual breakdown post-set to facilitate deep learning.
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