Mobile and Connected TV platform for sports fan engagement
YinzCam operates a multi-surface fan engagement platform spanning mobile apps, websites, and in-venue Connected TV — deployed across 150+ sports organizations globally. The tech stack reveals a mature, graphics-heavy mobile operation: native iOS and Android development (Swift, Kotlin, Objective-C), real-time big data processing (Spark, Hadoop, Hive), and emerging AR/computer-vision capabilities (OpenCV, Unity, Unreal Engine). Active projects show a shift toward AR stadium experiences and gameday automation, while the engineering-forward hiring mix (5 of 9 open roles) signals scaling of technical delivery rather than sales expansion.
YinzCam builds a unified digital platform for sports teams, leagues, venues, and events to manage fan experiences across mobile apps, websites, ticketing integration, and in-stadium video systems. The platform powers 150+ organizations in professional sports (NFL, NBA, NHL, MLS, Premier League, Liga MX, and others) and serves 150M+ mobile app installs globally. YinzCam's technology stack includes native mobile development, big-data analytics for fan segmentation and personalization, IPTV and in-venue video control, and emerging augmented-reality capabilities for stadium and at-home experiences. The company is privately held, based in Pittsburgh, and operates with 51–200 employees across engineering, design, product, and operations.
YinzCam uses native iOS (Swift, Objective-C) and Android (Kotlin) for mobile apps, Java and TypeScript/JavaScript for backend services, AWS with DynamoDB and Redshift for data infrastructure, Kubernetes and Docker for orchestration, and Apache Spark, Hadoop, and Hive for big-data processing. Recent additions include OpenCV and Unity/Unreal Engine for AR experiences.
YinzCam partners with 150+ sports and live event organizations, including teams and leagues from the NFL, NBA, NHL, MLS, UFL, English Premier League, Liga MX, Mexican Football Federation, Team Canada, and A-Leagues in Australia.
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YinzCam, Inc.'s technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
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