RideLvL is a mobile platform that uses AI to analyze skier and snowboarder video, overlaying technique feedback directly on the footage. It provides structured progression paths, goal tracking, and a social hub for sharing annotated clips, enabling athletes and coaches to monitor improvement without extra sensors.
Funding
Funding not disclosed
Founders
Product
Problem
Skier and snowboarder progression often relies on vague feedback, memory of coaching tips, and video that is only used for sharing moments, making it difficult to identify specific technique improvements and track development over time.
Solution
RideLvL turns ordinary action‑sport video into a structured learning tool by overlaying AI‑generated feedback on user‑captured footage. The platform automatically analyzes movement, highlights key technique metrics, and presents visual cues that clarify what worked and what didn’t. Users can follow curated progression paths, complete smart challenges, and set personalized goals, while the system tracks performance trends across sessions. Social features enable sharing of annotated clips and comparing progress with peers, fostering a community‑driven learning environment. All insights are delivered through a mobile app without the need for external sensors, allowing athletes to train at their own pace on the slope.
Target Audience
RideLvL is aimed at recreational and competitive skiers and snowboarders who want data‑backed feedback, as well as coaches and instructors seeking a simple tool to augment lesson follow‑up and athlete monitoring.
Features
- AI‑driven video analysis that extracts technique indicators and visualizes them directly on the skier’s footage
- Structured progression pathways with milestone‑based challenges to guide skill development
- Goal‑setting and performance tracking dashboards that show longitudinal improvement metrics
- Social sharing tools for annotated clips, peer feedback, and community challenges
- Sensor‑free mobile implementation that works with any smartphone video capture