Turn your content library into a connected intelligence asset
Media organizations hold vast libraries of video, editorial content, images, and metadata. Most of it is unsearchable, disconnected, and inaccessible to AI. Data Graphs connects content, context, and audience data in a single knowledge graph, making everything findable, monetizable, and ready for AI-powered products.
The missing layer in your content ecosystem
Media organizations generate more content than any system can effectively manage. Video archives in a DAM. Editorial in a CMS. Rights and talent data in a spreadsheet or a separate rights management tool. Audience data in an analytics platform. Sponsorship and commercial agreements somewhere else entirely. Each system knows its own content. None understands the connections between them.
Data Graphs provides the content intelligence layer that ties it all together. Every asset, a video clip, an article, an image, a talent profile, a sponsorship deal, is a node in a connected graph. A news story is not just a document in a CMS. It is linked to the people it mentions, the places it covers, the archive footage that supports it, the related stories that preceded it, and the audience segments most likely to engage with it. This connected context is what enables AI-powered search, contextual ad placement, personalized recommendations, and editorial workflows that actually reduce manual effort rather than adding to it.
Find any piece of content across your entire archive, instantly
Editorial and production teams at large media organizations spend significant time searching for specific content: an archival clip of a particular person, an interview from a specific year, footage from a past event. Traditional DAMs and CMS platforms rely on keyword tagging, and the quality of what you find depends entirely on the quality of the manual tagging that preceded it. With Data Graphs, every piece of content is enriched with structured semantic metadata and connected to related entities in the graph. An editor can search in natural language, "Find all interviews with this person from the last three years where they discussed this topic," and the graph traverses the relationships to surface the right material, regardless of how it was originally tagged.
Turn archive video into a monetizable, queryable asset
Most broadcasters and publishers have years of video archives that generate no value because they cannot be searched or linked to current content. With the Bitmovin and Data Graphs partnership, AI scene analysis generates rich scene-level metadata (people, objects, mood, setting, topic) which is ingested into Data Graphs as structured knowledge. Every scene becomes a queryable node connected to people, events, topics, and commercial data. A producer can find every scene featuring a particular athlete across a decade of archive footage in seconds. A sales team can demonstrate how many minutes of qualifying content a sponsor's brand appeared in, by program, by timecode, and by audience reach. What was an inert archive becomes a connected, monetizable intelligence asset.
Personalize content discovery without third-party data
Audience personalization typically requires either invasive data collection or expensive third-party data. Data Graphs enables a different approach: connecting your own first-party content and audience behavior data in a graph that surfaces genuinely relevant recommendations based on what your audience has engaged with and how content entities relate to each other. A user who has read three articles about a topic is connected in the graph to all related content across formats (articles, video, archive clips, podcasts) enabling recommendations that cross format and channel boundaries without requiring third-party enrichment. The intelligence lives in the connections within your own data.
See how Bitmovin and Data Graphs deliver intelligent video experiences.
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