This topic covers AI platforms used to personalize live sports and event broadcasts while improving accessibility for diverse audiences. It spans sports and video analytics, edge vision processing for low-latency feeds, and voice technologies for real-time captioning, multilingual dubbing and conversational agents. Recent industry shifts emphasize on-device/edge inference to meet latency and privacy needs, cloud APIs for scalable translation and voice rendering, and orchestration layers that make multi-agent workflows auditable and manageable. Key components include deep video understanding (for indexing, highlight detection and searchable clips), text-to-speech and voice-synthesis engines (for multilingual commentary, audio description and in-venue audio streams), and conversational/agent platforms (for interactive fan interfaces and automated operator workflows). For example, TwelveLabs-style video APIs enable frame-level indexing and summarization; Murf-like TTS platforms provide studio-grade, multilingual voiceovers and real-time voice APIs; DeepL-class machine translation supports high-quality multilingual captions and scripts; and enterprise agent platforms (Kore.ai, PolyAI, Observe.AI) supply conversational voice agents, governance, observability and real-time assist features needed in broadcast and contact-center integrations. Practically, these capabilities are used for personalized audio mixes, real-time captions and translations, automated highlight reels, accessibility features (audio description and improved caption accuracy), and venue-specific edge experiences. Implementation considerations include latency, scalability, content moderation, compliance with accessibility standards, and integration across broadcast, streaming and in-venue systems. As live-event audiences grow more global and regulation/expectations around accessibility tighten, these tool categories form the operational stack for delivering timely, personalized and inclusive live-sports experiences.