Experience how data, performance and visibility come together to shape the future of football talent discovery.

Problem statement

Many young athletes, especially outside professional academies, struggle to get noticed. Talent discovery today is still heavily influenced by personal connections, subjective opinions, and limited visibility. At the same time, scouts and clubs rely on fragmented and inefficient processes. Player evaluation is often based on short impressions rather than structured data, making it difficult to identify real potential. As a result, talented players remain undiscovered, scouting becomes time-consuming and inconsistent, and athletes lack feedback to improve their performance. What is missing is a system that combines visibility, performance, and objective evaluation in one place.

Project Goal

Allstars AI was created to make football talent discovery fairer, more transparent and data-driven. The platform enables players to showcase their performance through video while combining this with structured data and interaction. The goal is not only to increase visibility, but also to help athletes understand their development and support scouts and clubs in identifying relevant talent more efficiently. By connecting all stakeholders in one platform, Allstars AI creates a shared environment where performance, visibility and opportunity come together.

Developed Solution and its Benefit

Allstars AI introduces a unified platform that combines football performance, data-driven insights and community interaction in one connected ecosystem. Instead of relying on fragmented scouting processes and subjective evaluation, the platform creates a transparent environment where players can showcase their abilities, receive feedback and gain visibility based on real activity.

Athletes can upload highlights, training clips and match situations directly to the platform, where the content becomes visible in a dynamic community feed. Scouts and clubs can discover talents through a searchable player database with filters such as age, nationality and playing position, allowing for a more efficient and structured discovery process. In addition, users can interact through likes, comments and contact requests, creating direct connections between athletes, scouts and clubs. By combining video content with measurable data and interaction, Allstars AI enables a continuous and scalable approach to football talent discovery that benefits both players and organizations.

Instead of relying on isolated scouting methods, the platform enables:

• Real performance representation through videos

  Players can showcase their actual skills and match situations, allowing others to evaluate them based on authentic performance rather than static profiles.

• Continuous evaluation through dynamic scores

  Performance and visibility are translated into evolving metrics, such as the Momentum Score, which reflects a player’s current development and relevance.

• Community-driven visibility through interactions

  Likes, comments, shares and community engagement directly influence how visible a player becomes, creating a system where interaction supports discovery and growth.

Testimonial
«The project established an initial technical foundation for the Allstars AI platform and provided valuable insights into the complexity of building a modern football intelligence and player development system. Especially in the areas of UX, mobile experience, and system architecture, the collaboration helped us better understand the requirements for scaling the product further.»
Dominik Vida

Key terms

• Programming languagesTypeScript, C#
• Frontend StackNext.js, React, Tailwind
• Backend Stack.NET 9, ASP.NET Core
• DatabasePostgreSQL
• Deployment PlatformVercel, Fly.io, Neon, Cloudflare

Customer

Allstars AI GmbH
Dominik Vida
www.allstarsai.com

Team