Delmondo, through its Conviva platform, provides a leading global streaming analytics and digital intelligence solution designed to help streaming businesses grow competitively by acting within seconds of observation. Conviva delivers comprehensive, continuous, census-level measurement via real-time, server-side sessionization at scale, using a single sensor and pipeline powered by over 60 patents. Its platform supports marketers, advertisers, tech operations, engineering, and customer care teams in acquiring, engaging, monetizing, and retaining audiences effectively. Conviva serves major brands such as DAZN, Disney+, Hulu, Paramount+, Peacock, Sky, Sling TV, TED, and WarnerMedia, processing nearly 3 trillion streaming data events every day. It supports over 500 million unique viewers watching 200 billion streams annually across 4 billion applications and devices. The company is headquartered in Silicon Valley with offices globally in New York, London, Madrid, Bengaluru, Beijing, and Singapore. Founded by professors from Carnegie Mellon and U.C. Berkeley and backed by investors including NEA, Foundation Capital, and Time Warner Investments, Conviva goes beyond traditional analytics approaches by providing consumer-perspective insights for apps, websites, and AI agents through its Digital Intelligence Platform. This outcome-first platform uses patented stateful analytics and full-census client-side telemetry to analyze every interaction continuously, revealing behavior patterns tied to real-world outcomes such as trust, friction reduction, and revenue growth. Powered by patented Time-State Technology, the Conviva Digital Intelligence Platform offers real-time monitoring of AI agent sessions, links end-user behaviors to business outcomes and system performance, uncovers growth opportunities, accelerates issue resolution, and delivers AI-powered insights for flawless streaming experiences. This enables brands to reduce costs, improve reliability, build trust, and drive viewer engagement and revenue growth by replacing guesswork with predictable and outcome-based experience patterns.
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