Eluvio has introduced the first commercially available architecture that runs multimodal AI analysis directly inside the video streaming pipeline itself, eliminating the traditional workflow of copying files, processing them separately, and re-encoding the results. The company announced the new Universal & Dynamic Video Intelligence architecture at IBC 2026, featuring 17 built-in AI models, an open-model inference engine, and agentic orchestration capabilities designed for broadcasters and content creators working with live sports, premium video, and on-demand libraries.

How Does Inline Video AI Processing Work Differently?

Traditional video AI workflows require multiple steps that consume time and resources. Video files are copied from their original location, moved into separate cloud processing systems, analyzed by AI models, stored again, and frequently re-transcoded to create derivative content like highlights or vertical videos. Eluvio's approach eliminates this entire chain of movement and duplication.

Instead, AI models operate directly against video as it flows through the Content Fabric media pipeline. The system produces frame-accurate labels, embeddings, vectors, transcripts, and metadata that are immediately available for personalization, clipping, and derivative creation. Because there are zero file copies, zero media movement, and zero re-transcoding, the entire workflow happens in place against the source media.

"Eluvio AI is fundamentally different from point AI solutions because the intelligence runs inline, with frame accuracy, across live and archival content, without the cost and delay of copying and re-transcoding media before it can be processed," said Michelle Munson, CEO and co-founder of Eluvio. "The AI is built directly into the video pipeline of the Content Fabric, rather than added afterward as a separate cloud workflow."

Michelle Munson, CEO and co-founder of Eluvio

What Specific AI Capabilities Does the Platform Include?

Eluvio's expanded AI inference engine now includes 17 built-in models and processors that span multiple types of analysis and content generation. The platform supports frame-level and video-level inference, meaning AI models can analyze individual frames, randomly sampled frames, segments, shots, scenes, or entire videos depending on the use case.

• Temporal Analysis: Shot, beat, and scene segmentation identifies meaningful structural boundaries within content, providing the foundation for search, summarization, clipping, and composition.
• Multimodal Intelligence: The system processes video, audio, text, images, pose data, and multidimensional telemetry at frame and segment level, with no secondary metadata alignment step required because intelligence is generated against the underlying media timeline.
• Motion and Identity Recognition: New capabilities include custom motion identification, expert focus tracking, speaker detection and identification, celebrity identification, and player identification, particularly useful for live sports coverage.
• Transcription and Language: Multi-language speech-to-text produces time-aligned transcripts that combine with speaker recognition and other visual intelligence.
• Live Sports Generation: Just-in-time vertical video generation combines live sports motion analysis, expert focus tracking, and per-player jersey identification to create derivative content automatically.
• Content Summarization: The platform identifies key moments within video and generates personalized synopses and summaries by segment or across entire titles and events.

What Makes This Architecture Practical for Broadcasters?

The inline processing model has immediate practical implications for media companies. Because AI intelligence can directly drive the video itself, broadcasters can enable frame-accurate search, personalization, clipping, highlights, vertical video generation, compliance reels, and summaries for both live and on-demand content without the delays and costs of traditional workflows.

The system includes an extensible API that allows third-party and custom models to be incorporated without changing the underlying media workflow or moving the media. This means broadcasters can add proprietary AI models or integrate specialized tools without disrupting their existing infrastructure.

Eluvio also introduced the next-generation Eluvio Video Intelligence Editor (EVIE), which includes expanded features for managing AI intelligence. EVIE Titles provides a unified view of AI intelligence across content, a new AI Runtime manages inference, and media-synchronized visualization displays AI data by frame, shot, beat, and scene. The platform also features zero-copy AI clipping and composition, automatic dynamic vertical video playout, and a new Eluvio Model Context Protocol (MCP) API that exposes major functions for agentic orchestration.

When Will Broadcasters Be Able to Access These Capabilities?

Eluvio will demonstrate these capabilities live at IBC Show 2026, scheduled for September 11 through 14, 2026, at RAI Amsterdam. The company will have booths in Hall 8, Stand 8.MS5, and the Future Tech Zone, Hall 14, Stand 14.A58.

The announcement represents a shift in how video AI infrastructure is being designed for the broadcast and streaming industry. Rather than treating AI as a separate post-production step, Eluvio has integrated it directly into the media distribution pipeline itself, allowing intelligence to become immediately actionable for personalization and monetization at scale.

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