A broadcast media company producing 18 hours of live content daily across three channels needed to generate metadata, detect highlights, check compliance, and prepare content for digital distribution. The manual process involved a team of 25 working in shifts to tag, log, and review content. Turnaround time from broadcast to digital availability averaged 6 hours.

The Architecture

The system processes live broadcast feeds in near-real-time through parallel analysis pipelines.

Live ingest - Broadcast feeds are captured as continuous streams and segmented into 5-minute processing chunks with 30-second overlap to avoid splitting events across chunk boundaries. Each chunk enters the processing pipeline immediately, enabling near-real-time analysis.

Speech analysis - Real-time transcription with speaker identification produces a timestamped transcript. An LLM processes the transcript to extract topic segments, key quotes, named entities (people, organizations, locations discussed), and content warnings (profanity, sensitive topics).

Visual analysis - Frame analysis identifies on-screen graphics (lower-thirds, score overlays, breaking news banners), scene transitions, faces of known personalities, and visual content categories (interview, gameplay, studio, field report). For sports content, the system detects scoring events, replays, and celebration reactions.

Highlight detection - A model trained on historically high-engagement moments identifies potential highlights by combining audio energy (crowd noise, commentator excitement), visual indicators (replays, close-ups), and transcript cues (superlatives, significant event descriptions). Detected highlights are clipped with 10 seconds of lead-in and marked for editorial review.

Compliance checking - The system flags potential compliance issues: uncleared music (audio fingerprinting), visible brand logos that need clearance, content that may violate broadcasting standards, and rights-restricted content that cannot be distributed digitally.

Key Lessons

Near-real-time processing required architectural compromises. The full analysis pipeline took 3 minutes per 5-minute chunk. To deliver metadata within minutes of broadcast, the team implemented a two-pass approach: a fast first pass producing basic metadata (transcript, scene detection) within 90 seconds, and a thorough second pass adding enriched metadata (highlights, compliance flags) within 5 minutes.

Highlight detection was subjective and genre-dependent. What constitutes a highlight in a sports broadcast (scoring play, controversial call) is entirely different from a news broadcast (breaking development, key interview quote) or entertainment show (reveal, performance climax). Genre-specific highlight models trained on editorial team selections outperformed a single general model by 40%.

Compliance checking paid for the entire system. A single compliance violation (broadcasting uncleared music, showing restricted content on digital platforms) could cost more than the annual system budget in licensing penalties. Automated compliance checking caught an average of 8 potential violations per week that would have reached digital distribution under the manual process.

Editorial curation remained essential. The system generated 3x more highlight candidates than editors could use. The value was not in eliminating editorial judgment but in presenting editors with pre-clipped, pre-tagged candidates instead of requiring them to watch hours of content. Editors focused on selection and sequencing rather than discovery.

Results

Time from broadcast to digital availability decreased from 6 hours to 45 minutes. Metadata completeness improved from 60% (manual tagging) to 95% (AI-generated with editorial review). Highlight clip production increased from 15 to 50 per day across all channels. Compliance violation incidents in digital distribution decreased by 85%. The content team was redeployed from manual logging to creative editorial work.