Garde utilizes machine learning and pose estimation technology to provide real-time feedback and performance analytics for fencers, enabling precise technique improvement. The platform enhances training efficiency for athletes and streamlines progress tracking for coaches, addressing the challenges of skill development in fencing.
Funding
Funding not disclosed
Founders
Product
Problem
Fencers often struggle to identify and correct subtle flaws in their technique without expert, real-time feedback. Traditional coaching methods can be limited by the coach's ability to observe and analyze every movement, hindering efficient skill development. Furthermore, tracking progress and performance metrics can be challenging without objective data and analytics.
Solution
Garde provides an AI-powered platform that delivers real-time feedback and performance analytics to fencers and coaches. Using pose estimation technology, Garde analyzes a fencer's form and provides immediate, actionable insights to improve technique and correct improper form. The platform offers video dashboards and detailed analytics, enabling coaches to efficiently track student progress and provide targeted feedback. Garde also supports tournament organization by streamlining participant signups and providing a centralized platform for accessing videos and results.
Target Audience
Garde targets individual fencers seeking to improve their technique, coaches looking to enhance training efficiency, and tournament organizers aiming to streamline event management.
Features
- AI-powered pose estimation for precise analysis of fencing form
- Real-time feedback to athletes for immediate technique correction
- Video dashboard for coaches to review fencer performance
- Detailed analytics and key metrics for tracking progress
- Tournament management tools for streamlined organization
- Mobile app for on-the-go access to performance breakdowns and personalized drills (coming soon)
- Generative Sports Expert (GSX-a1) providing coaching insights and strategies
- Frame-by-frame movement breakdowns for tailored feedback