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In the competitive world of professional football transfers, Hiddenkick AI is an ML-powered scouting platform helping clubs make smarter decisions using the data they already have. Today, we are interviewing Anis Hamak to learn how their computer vision technology evaluates dynamic player compatibility on the pitch to give clubs a crucial competitive edge.

What does Hiddenkick AI do? And why is now the time for it to exist?

Hiddenkick AI is an ML-powered scouting platform already deployed at MCA. We help clubs make smarter transfer decisions using data most teams have but don't know how to use. We are building a computer vision module aiming to solve one of the most difficult questions in football: how compatible are two players on the pitch? We are extracting tactical configurations and movements to identify compatibility scores not only from row data but also from dynamic data. Now’s a good time for Hiddenkick AI to exist because the financial stakes in football have never been higher, and motion intelligence has finally advanced enough to measure dynamic player compatibility accurately without requiring a massive data science budget.

Who does your Hiddenkick AI serve? What’s exciting about your users and customers?

Football clubs that can't afford to get transfers wrong or hire a data science department. That means mid-table and lower-budget clubs who rely on smart recruitment rather than spending power. Technically, the end users are scouts and sporting directors. But the real buyer is any club that knows the gap between a good signing and a bad one is the difference between staying up and going down.

What technologies were used in the making of Hiddenkick AI? And why did you choose the ones most essential to your tech stack?

Hiddenkick AI leverages machine learning and advanced computer vision technologies to process dynamic tactical data on the pitch. By building proprietary modules focused on extracting motion intelligence, the platform can interpret complex football configurations beyond standard row data. This specialized tech stack was chosen because traditional dashboards fall short of providing the genuine tactical intelligence needed for accurate, dynamic player compatibility scoring.

What is traction to date for Hiddenkick AI? Around the web, who’s been noticing?

Hiddenkick AI has begun establishing its professional presence within the football analytics community, primarily through its LinkedIn company page. The platform has already proven real-world utility with an active deployment at MCA, and it consistently generates organic interest with 1-3 inbound requests per week from clubs seeking smarter recruitment solutions.

Hiddenkick AI earned a 44 proof of usefulness score (https://proofofusefulness.com/report/hiddenkick)

What excites you about this Hiddenkick AI's potential usefulness?

Football clubs make million-pound decisions based on gut feel and highlight reels. That shouldn't still be true in 2026. What excites me is the asymmetry. A well-designed similarity model doesn't care whether a player is at PSG or a second-division club in Algeria. It finds the profile. That means a club with a £2m transfer budget can access the same quality of insight as one spending £200m. They just haven't had the tool to act on it until now. The deeper opportunity is that structured event data still only captures what happened, not how it happened. We're building toward motion intelligence, understanding pressing behaviour, movement off the ball, defensive compactness, the things that actually explain why a player fits a system. That's where the real edge is, and almost nobody is there yet. Scouting is a market that's been sold expensive dashboards dressed up as intelligence. Hiddenkick AI is building the actual intelligence layer.

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