AI Infrastructure for Screambox
AI Infrastructure for Screambox
AI Infrastructure for Screambox
AI-powered backend for personalized content delivery, user behavior analytics, and automated curation
AI-powered backend for personalized content delivery, user behavior analytics, and automated curation
AI-powered backend for personalized content delivery, user behavior analytics, and automated curation
Screambox wanted more than just a video catalog. They wanted to create an experience that felt personal, predictive, and immersive. I led the development of an AI-integrated backend that learns from every interaction, curates dynamic experiences, and powers features that rival any major OTT platform.
Screambox wanted more than just a video catalog. They wanted to create an experience that felt personal, predictive, and immersive. I led the development of an AI-integrated backend that learns from every interaction, curates dynamic experiences, and powers features that rival any major OTT platform.
Screambox wanted more than just a video catalog. They wanted to create an experience that felt personal, predictive, and immersive. I led the development of an AI-integrated backend that learns from every interaction, curates dynamic experiences, and powers features that rival any major OTT platform.
Client
Screambox
Services
Recommendation Engine Generative AI for Metadata Conversational Agent Content Optimization Layer
Industries
Streaming
Date
March 2025
The Screambox backend operates as a layered intelligence stack. Generative models run as microservices, agents handle real-time queries, and all systems log back into a centralized user behavior model. The architecture is modular, API-first, and tuned for streaming workloads at scale. Screambox moved from static catalogs to living, adaptive interfaces. Viewers found content faster. Discovery became conversational. Engagement and retention improved. The brand evolved from a genre platform into a smart entertainment engine
The Screambox backend operates as a layered intelligence stack. Generative models run as microservices, agents handle real-time queries, and all systems log back into a centralized user behavior model. The architecture is modular, API-first, and tuned for streaming workloads at scale. Screambox moved from static catalogs to living, adaptive interfaces. Viewers found content faster. Discovery became conversational. Engagement and retention improved. The brand evolved from a genre platform into a smart entertainment engine
The Screambox backend operates as a layered intelligence stack. Generative models run as microservices, agents handle real-time queries, and all systems log back into a centralized user behavior model. The architecture is modular, API-first, and tuned for streaming workloads at scale. Screambox moved from static catalogs to living, adaptive interfaces. Viewers found content faster. Discovery became conversational. Engagement and retention improved. The brand evolved from a genre platform into a smart entertainment engine
This was not just a streaming backend. It was a complete AI-powered content brain. Built to watch the viewer as much as the viewer watches the screen. Screambox now stands at the frontier of intelligent entertainment
This was not just a streaming backend. It was a complete AI-powered content brain. Built to watch the viewer as much as the viewer watches the screen. Screambox now stands at the frontier of intelligent entertainment
This was not just a streaming backend. It was a complete AI-powered content brain. Built to watch the viewer as much as the viewer watches the screen. Screambox now stands at the frontier of intelligent entertainment