Get richer, more accurate tagging
Analyze both visual and dialogue signals
Content Understanding
A system that sees the full picture
Understanding content at scale is not a model problem. It is an orchestration one. Coactive coordinates specialized AI models across every modality, fuses their signals into a single structured layer, and connects that intelligence to the context surrounding your content. The result is a rich, actionable understanding of your entire library.
Model-independent infrastructure
More signals = better tags
Quickly identify relevant segments
Uses include:
highlight reels
story arc creation
ad insertions
contextual advertisement
trust and safety (content moderation)
… and more!
Model-independent infrastructure
Metadata tagging without tears - and without high processing costs
Coactive replaces time-consuming metadata workflows with dynamic, AI-powered tagging that adapts to your content and use case. Customers reduce tagging time from days to minutes—while improving consistency, precision, and downstream reuse.
Multimodal tagging
Automate labeling across visuals, audio, and dialogue—no manual stitching required.
Reusable taxonomies and tags
Reduce duplication across teams and projects.
Built-in evaluation tools
Validate accuracy instantly—no time-consuming QA passes needed.
Unlimited fine-tuning with no extra cost
Enable fast iteration using stored embeddings—with no retraining or per-run fees.
Model-independent infrastructure
Create detailed and accurate tags, fast.
LLM-powered prompt suggestions
Get prompt design guidance from AI.
Intuitive Tag Preview page
Preview shot-level results directly in the UI.
Improved visual labeling workflow
Bulk review uncertain assets and guide tagging with uploaded labels.
Model-independent infrastructure
Tag in subtle, sophisticated, and revenue-driving ways
Identify all of the streetlights? Easy! Identify nuanced concepts like “female empowerment” or “athleisure,” or proprietary items like your new “Phoenix 3000” product? Hard. But Coactive makes it easy and fast.
Identify conceptual content
You know “female empowerment” when you see it. But does your tagging solution know it? Just quickly define a concept for Coactive, and it can identify or find subtle, abstract, or complicated concepts.
Identify emerging celebrities or new objects
Overnight stars or new products probably weren’t in a foundation model’s training set, so they can’t find or tag what you’re looking for. But with Coactive, you can quickly tell Coactive what to look for, and it finds it. Without retraining or reprocessing cost.
Identify subtleties like “athleisure” - even in your own way
Do you define athleisure as yoga pants? Or do you define it as an unstructured blazer? Just define it in the way that makes sense to your business, and Coactive will classify or search for those items in the way you want it to, without expensive retraining or reprocessing.
Model-independent infrastructure
Gain confidence with built-in Ground Truth evaluation
Automated Ground Truth generation
Auto-generate pseudo-ground truth with LLMs, removing the manual effort of building evaluation datasets.
Built-in performance validation
Track F1, precision, recall, and accuracy with interactive visualizations across thresholds.
Self-service quality control
Transform tag evaluation efforts from hours to minutes. Review ground truth at keyframe and shot-levels, 1-click inaccurate item removal, and adjust precision-recall with interactive controls.
Why content understanding is hard
A compound AI system, not a single model
The default approach to content understanding is straightforward: pass a video through a large model, get labels back. It works for demos. It breaks at scale.
True content understanding requires processing visual composition, spoken dialogue, ambient audio, on-screen text, shot structure, and narrative arc simultaneously, then fusing all of those signals into a coherent, hierarchical data model that spans shots, scenes, episodes, and series. It means preserving the metadata your content already carries and enriching it rather than replacing it. It means connecting content signals to external context (impressions, audience data, business KPIs) so that understanding compounds over time.
This is a compound AI system that orchestrates multiple models, databases, and services with dozens of orchestrated models, not a single inference call. That distinction is the difference between a demo and a production deployment.
What the Platform Processes
Every signal. Every modality. One Pass.
When content enters the Coactive Multimodal AI Platform, the system processes it across every dimension simultaneously. Nothing is deferred. Nothing requires a second pass.
Your content is immediately searchable through Content Discovery, queryable through Content Analytics, and ready for classification through Dynamic Tags. The foundation is built in a single pass.
Narrative Metadata
Automated content intelligence
Early Access
Narrative Metadata automatically generates rich, structured descriptions of your image & video content. Rather than relying on manual logging or stitching together multiple AI services, a single capability produces narrative-level outputs across multiple dimensions:
Narrative text captures what your content is about and how to present it through summaries, synopses, and descriptions.
Content dimensions classify how content is constructed and experienced, including genre, mood, subject, and format.
All outputs are queryable in SQL and viewable through the platform UI. Narrative Metadata makes videos instantly more navigable, discoverable, and searchable, without hours of manual review. Available in early access for qualifying customers.
Celebrity Detection
Identify who appears. Everywhere they appear.
Enroll the faces that matter to your business. The platform builds a recognition model scoped to your enrolled individuals and identifies their appearances across your entire ingested library, tagged at the shot level.
Appearances surface automatically through Content Discovery, Dynamic Tags, and Content Analytics. No manual review. No episode-by-episode search.
Common applications: Talent visibility across catalog and archive. Rights and clearance workflows. Compliance monitoring. Sponsor and brand ambassador tracking.
The Orchestration Advantage
Model-agnostic by design. Better by default.
The Coactive platform does not depend on a single model for content understanding. It evaluates, selects, and orchestrates the best model for each task across each modality, and it evolves as the model landscape evolves.
When a new vision model outperforms the current one on scene classification, the platform adopts it. When a new speech model improves transcript accuracy, your existing content benefits. Your intelligence layer improves without re-ingestion, without re-configuration, and without vendor lock-in to a model provider's roadmap.
This is what it means to be a platform rather than a model wrapper. The AI improves. Your data stays. Your intelligence compounds.
From Understanding to Action
Understanding is the foundation. Here is what you build on it.
Content Understanding creates the signal layer. The platform's other capabilities turn those signals into decisions, workflows, and measurable outcomes.
Define what matters to your business.
Dynamic Tags lets you score every piece of content against the taxonomy you define. Your categories. Your language. Your intent. Applied to every signal Content Understanding generates, at scale.
Understand content in context.
Content Analytics joins the signals from Content Understanding with external data: impressions, performance metrics, audience segments, and business outcomes. Content should be understood not just for what it contains, but for what it means in the context of your business.
Find any moment, instantly.
Content Discovery makes every signal searchable through natural language, structured queries, and multimodal search. The intelligence Content Understanding creates becomes immediately accessible.
