People

Founder-led. Field-tested with private coaches and athletes.

BoxScore AI is led by Ryan Alvin and shaped through ongoing feedback from coaches, trainers, and athletes using the platform in real training environments.

R

Founder

Ryan Alvin

Founder, CEO, and product lead

Ryan leads BoxScore AI's product direction, technical architecture, coach workflow, athlete experience, AI review systems, and production rollout.

View Ryan on LinkedIn
Computer vision foundation

Ryan's education and technical work in computer vision inform how BoxScore AI approaches video, evidence, and athlete-development signal.

Applied AI product builder

He develops the product across AI workflows, backend systems, frontend experience, cloud deployment, and operational reliability.

Coach-centered execution

The product is shaped with coaches and athletes so the technology stays grounded in real training environments.

  • Product owner: defines how coaches assign work, review film, approve feedback, and manage athlete progress.
  • Technical lead: develops the platform's video intelligence workflows, product surfaces, API, deployment systems, and operational tooling.
  • Customer development: works directly with early coaches and athletes to ensure the product supports real training habits.
  • Operating principle: AI should help organize evidence and draft useful feedback while coaches remain in control.

Early access contributors

Guided by real training environments.

BoxScore AI is being refined with select beta contributors, including coaches, trainers, program athletes, and professional athletes. Their feedback helps validate product quality before broader release.

Coaches and trainers

Evaluate assignment creation, review queues, feedback approval, and day-to-day program workflows.

Professional athlete input

Provides perspective on video instructions, feedback quality, and whether AI-assisted review feels actionable for high-level training.

Program athlete testing

Helps validate mobile upload, assignment clarity, and the athlete experience across repeated training cycles.

Participant confidentiality. Beta contributors' identities, programs, and training details remain confidential while their feedback informs product development.

Early feedback focus areas

  • Assignment clarity for both coaches and athletes.
  • Coach priority capture before AI-assisted review begins.
  • Review and approval flow before athlete-facing feedback is released.
  • Evidence summaries that are clear, concise, and appropriate for training decisions.

Focused leadership, practical validation

BoxScore AI is founder-led and developed with input from a select group of coaches, trainers, and athletes. The goal is to earn trust through useful workflows, careful feedback loops, and measurable product quality.