Forma Run

Forma Run is an AI-powered digital coaching platform designed to enhance a runner's performance and technique. Developed by a team focused on biomechanics and machine learning, its core value lies in making expert-level running form analysis accessible to anyone with a smartphone, eliminating the need for expensive in-person gait analysis sessions. By simply uploading a video, users receive actionable insights that were previously available only to professional athletes, empowering them to run more efficiently and reduce injury risk.

Key features include the ability to upload running videos from any angle for comprehensive analysis, receiving detailed breakdowns of metrics like cadence, ground contact time, and posture alignment. The AI provides specific corrective feedback and drills tailored to the user's form, tracks progress over time with visual comparisons, and offers educational content on running mechanics. It supports analysis for various running surfaces and can highlight asymmetries or imbalances between left and right sides.

What makes Forma Run unique is its proprietary computer vision models trained specifically on human movement kinematics, allowing it to interpret subtle biomechanical cues from standard video without requiring specialized sensors. The platform operates entirely via a web application, ensuring broad accessibility without mandatory app downloads, and maintains user privacy by processing videos without storing identifiable biometric data long-term. Its feedback system is designed to mimic the observational eye of a seasoned coach, translating complex data into simple, understandable recommendations.

Ideal for amateur and recreational runners seeking to improve their technique and prevent common injuries, as well as for coaches looking for a tool to remotely assess their athletes' form. It is also highly valuable for individuals returning to running after a break or injury who need guided rehabilitation, and for fitness enthusiasts who want data-driven insights to complement their training logs without investing in wearable hardware.