Every piece of intelligence the platform generates lands in a structured, SQL-queryable data model. Your content library becomes a data asset you can analyze, report on, and integrate with the tools you already use.
The Coactive Multimodal AI Platform automatically organizes your content into a hierarchy of structured tables that mirrors how media is produced and consumed.
Videos contain scenes. Scenes contain shots. Shots have moments. Each level carries its own intelligence: tag scores, concept probabilities, transcript segments, and metadata. All levels are joinable. All levels are queryable.
This is not a flat export. The media data model preserves the temporal and compositional relationships within your content so analytics reflect how the content was actually made and experienced.
The media data model carries structured identifiers at every level, making it straightforward to join Coactive intelligence with your own performance data. Combine tag scores with show ratings, ad impressions, click-through rates, or conversion metrics to understand which content drive outcomes. The intelligence layer becomes a connective tissue between what's in your content and how it performs in market.
The built-in query engine lets you run performant SQL directly against your content data. Filter by tag scores. Join across content hierarchies. Aggregate at any level. No need to export to another tool first.
What is in your library? Break down your content by tag distribution, genre, mood, or any custom classification. Understand the shape of your inventory before you sell or program it.
How much content matches a given advertiser brief or targeting criteria? Quantify available inventory against Dynamic Tag definitions to support planning, forecasting, and pricing conversations.
Which contextual packages have the highest match rates? How does tag coverage vary across shows, seasons, or content types? Join content intelligence with campaign performance data to understand which moments, contexts, and placements drove measurable outcomes.
Which content themes are trending across your library? Where are the concentrations of specific moods, subjects, or visual patterns? Surface editorial intelligence that informs programming, acquisition, and content strategy.
Content Analytics is not a closed system. Query results and structured data are accessible through the API for integration with external analytics environments, data warehouses, business intelligence platforms, and custom reporting systems.
Export structured outputs, including raw tag scores, concept probabilities, and transcript data, to S3, CSV, or directly into data lakes like Delta Lake. Load into Python, R, PySpark, or your preferred BI tool for analysis alongside your own performance data.
Data science teams use exported scores and probabilities to test classification thresholds, benchmark content performance across networks or campaigns, and build custom models in their own environments. The data model stays consistent wherever it goes.
