From Keywords to Intent
Traditional keyword search is outdated. Users often don't know what they want to watch — they know how they want to feel.
Old: Keyword Search
- "Breaking Bad"
- "Action movies"
- "Tom Hanks"
New: Intent Search
- "Something to make me cry"
- "Feel-good family movie"
- "Tense thriller for date night"
How AI Semantic Search Works
1
Natural language understanding
AI parses the query to understand intent, not just keywords.
2
Intent classification
Maps query to categories: mood, theme, social context.
3
Semantic matching
Finds content with matching emotional/thematic metadata.
4
Ranking by relevance
Prioritizes best matches using ML models.
2026 Examples
Platforms implementing AI-driven smart search:
- Gizmott — natural language content discovery
- Veltris — mood-based recommendation engine
- Major streamers — testing voice search with intent understanding
Required Metadata for AI Search
AI search requires advanced metadata beyond basic genre/cast:
Mood tags
Happy, sad, tense, relaxing, inspiring.
Emotional metadata
Expected emotional journey/arc.
Scene-level descriptors
What happens in key scenes.
Theme tags
Underdog story, revenge, redemption.
EPG Service Solution
Implementing AI search?
EPG Service provides enriched metadata with mood tags, emotional descriptors, and theme classification for semantic search.
Get in Touch