Multimodal AI 101 for CTV
How AI understands video content for contextual CTV advertising
Executive Summary
$48B
Connected TV (CTV) is the fastest growing major channel for marketers, with growth led by increasing audience adoption and ad spend that’s projected to reach $48 billion by 2028.
As CTV continues its meteoric rise, advertisers face the challenge of ensuring their messages appear in the most relevant contexts at scale. Just as Google’s AdSense revolutionized the web and its monetization through contextually relevant digital ads, a similar transformation is now occurring in CTV through multimodal AI technology.
Contextual advertising helped drive the commercial success of the web by matching ads to content people were already engaging with. Now the same principle is being applied to CTV with unprecedented precision through advances in multimodal AI. But here’s a crucial difference: in CTV, the most sophisticated contextual targeting occurs at the scene-level rather than simply the program level. Multimodal AI can achieve this granular level of analysis to create an “AdSense for CTV” that truly understands video content like a human expert would. Until now, traditional contextual advertising in CTV has been limited by its reliance on keywords, genre or program-level categorization. The new generation of multimodal AI technology for CTV is transforming how we are able to analyze video content, generate corresponding metadata and then match scenes with appropriate advertisements.
This white paper examines how multimodal AI – defined as technology that simultaneously analyzes multiple dimensions of video content including visuals, audio, captions, and metadata – is revolutionizing contextual advertising in CTV. By processing video content at the scene level through multiple parallel pathways, these systems achieve a human-like understanding.
The shift toward multimodal contextual advertising comes at a critical moment for the industry. With growing privacy regulations limiting personal data collection and the deprecation of third-party cookies, contextual solutions offer a compelling alternative that respects viewer privacy while delivering powerful targeting capabilities at a scale that would be unfeasible for human labor. For advertisers seeking to connect with audiences in today’s highly fragmented media landscape, multimodal AI provides an unprecedented ability to find the right moments for messages without relying on personal data.
The Evolution of Contextual Understanding in Advertising
With 340M Americans in your total addressable market across all age groups and living in virtually every region of the nation, your marketing strategy needs to be laser-focused. Instead of targeting all potential customers (which is, arguably, virtually everyone), it’s likely your team will explore and leverage a combination of targeted strategies to reach customers in the right place at the right time through tools such as AdSense, influencer campaigns with appeal for your fastest-growing audience segments or out-of-home placements in strategic locations.
Because your team is well aware of CTV’s designation as the fastest growing major channel for marketers today, of course they’ll want to place ads there. But here’s the challenge: when it comes to CTV, traditional contextual approaches have struggled to keep pace with both consumer expectations and advertisers’ needs. Genre- and text-based contextual targeting that worked well for websites doesn’t translate as effectively to the rich, multidimensional world of video content.
With so many options for CTV advertising, the only solution is to be smart with your spend
The smart ad spend is to place ads in front of audiences in moments where they’ll be most receptive to your message – just like you would with AdSense, influencer campaigns or strategically-chosen out-of-home placements. Multimodal AI is what unlocks this for CTV at the scene level – and at scale – across content programming libraries.
Thanks to its ability to move beyond simple keyword matching to achieve a deeper, more human-like understanding of video content, Multimodal AI represents the next evolutionary leap in contextual advertising for CTV, one that can drive more effective and precise ad placements and do so at the scale required by the sheer volume of programming that exists on ad-supported streaming platforms today.
Contextual advertising’s slow arrival to CTV – explained
Contextual advertising has been a cornerstone of digital marketing since Google’s AdSense and others pioneered the approach of matching advertisements to webpage content. This fundamentally transformed online advertising by creating a win-win scenario: users encountered more relevant ads, and advertisers reached consumers in moments when they were already engaged with related content. Thanks to its complexity, video content presents a different set of technical challenges.
These challenges have kept contextual targeting for CTV limited. The limitations of traditional approaches include:
These challenges have kept contextual targeting for CTV limited. The limitations of traditional approaches include:
Program-level targeting: Most contextual solutions for CTV categorize content only at the show or program level, yet a single episode can easily contain diverse scenes with completely different contextual opportunities. Because of this, grouping entire shows under a single category causes advertisers to miss key moments that align with their brands. For instance, a travel brand targeting “adventure” content might miss powerful opportunities in dramas, documentaries or comedies with rich travel scenes.
Limited dimensionality: Traditional solutions that analyze only one aspect of content (such as genre, text captions or) miss the rich contextual cues video offers — like emotional tone, sentiment, visual storytelling, or non-verbal audio. This reduces ad relevance and effectiveness.
Rigid taxonomies: Fixed or pre-defined category systems can’t capture the nuanced meaning and relationships that exist within video content. A “romantic comedy” often includes family moments, workplace scenes, or travel adventures — all of which present distinct ad opportunities.
Binary brand safety: Traditional approaches to CTV ad targeting focus on avoiding negative contexts rather than finding optimal positive ones.
These challenges have created a significant gap between the contextual targeting possibilities that advertisers expect and what the majority of currently available CTV advertising technology delivers.
aAt the same time, viewership continues to fragment across a growing array of streaming platforms, with Free Ad-Supported TV (FAST) reaching 66% of American consumers in 2024 and expected to grow significantly in the coming years. To that end, a recent Gracenote report found that the number of FAST channels increased 42% in the second half of 2024 alone.
This fragmentation makes it increasingly difficult for CTV advertisers to reach audiences efficiently through traditional means, creating the need for more sophisticated approaches to contextual understanding and targeting.
What Makes Multimodal AI Different: Beyond Single Dimension Analysis
Multimodal AI represents a fundamental shift in how machines understand video content.
In everyday experience, we don’t separate what we see from what we hear – we process all available signals together to form a complete understanding of a situation. Multimodal AI brings this same integrated approach to video content analysis:
By analyzing these different modalities in parallel and then combining their insights, multimodal systems achieve a dramatically more comprehensive understanding of content than single-modality approaches. This capability is essential to working effectively with video because video content is inherently complex. Unlike web content made up of text and images, each scene of a video combines visuals, dialogue, background sounds and emotional cues — elements that, when analyzed together, provide a far richer understanding than any one dimension alone.
At Anoki, we pair multimodal AI with our own unique, expert-based modular architecture that combines specialized expert models that work together while remaining independently updatable. We use this approach to make our solutions scalable, flexible, and responsive to real-world advertising needs.
Scene-level granularity
Advances in multimodal AI have opened the door to the more granular analysis of video content that is driving scene-level contextual advertising in CTV.
Traditionally, contextual solutions have typically categorized content at the genre level – for example, an entire show or movie might be labeled as “comedy” or “drama.” Our multimodal AI system takes a more precise approach by breaking content down into scenes, typically 30-60 seconds in length.
This scene-level granularity is transformative:
A single program can contain many different contexts and emotional moments.
Advertising opportunities vary dramatically from scene to scene, with the most impactful ad placements often depending on specific moments rather than general program categories.
Scene-level intelligence allows advertisers to target specific moments that align with their messaging.
For example, rather than simply targeting “cooking shows,” a kitchen appliance brand can match campaigns to scenes where characters are actively preparing food – regardless of whether those moments appear in cooking programs, dramas, sitcoms, or reality shows.
A shift from brand safety to suitability
Traditional brand safety approaches focus on avoiding problematic content categories. Multimodal AI enables a shift from safety to suitability – not just steering clear of unsuitable contexts but actively identifying the most relevant and impactful moments for specific brand messages.
This paradigm shift is possible because multimodal systems understand content at a deeper level:
Emotional context: Identifying the mood and feeling of a scene
Activity context: Understanding what’s happening and who’s involved
Thematic context: Grasping the underlying topics and themes
As we’ll explore further, this multidimensional understanding opens new possibilities for precision targeting that weren’t possible with previous generations of contextual CTV technology.
Our unique expert-based modular architecture
Working alongside the power of multimodal AI itself and core to our approach at Anoki is a unique expert-based modular architecture designed to maximize both precision and flexibility. This system combines specialized expert models, each trained to analyze distinct aspects of video content such as visuals, audio, text, or metadata. These expert models operate collaboratively, yet remain independently updatable.
This modular design ensures our system remains agile, adaptable, and consistently optimized to meet evolving advertiser needs.
We can update components to incorporate improvements to visual detection, enhance cultural context recognition or adjust brand safety parameters without retraining or rebuilding the entire system.
Under the Hood: How Multimodal Scene-Level Analysis Works
To truly appreciate the revolutionary capabilities of multimodal AI for contextual advertising, it’s valuable to understand how these systems actually work. While the underlying technology is complex, the core principles can be explained in accessible terms.
To truly appreciate the revolutionary capabilities of multimodal AI for contextual advertising, it’s valuable to understand how these systems actually work. While the underlying technology is complex, the core principles can be explained in accessible terms.
Parallel analysis across multiple modalities
After segmentation, specialized expert models analyze different aspects of content simultaneously. An advanced multimodal system might incorporate dozens or more different AI models working in concert, each specialized for specific tasks:
Visual analysis: The system samples frames from each scene and processes them through vision encoders to capture visual elements like objects, places and actions.
Audio processing: Audio segments are analyzed using specialized audio encoders that identify sound effects, music, ambient noise, and other non-verbal auditory elements.
Caption/transcript analysis: Speech is converted to text and processed through a text encoder.
Metadata extraction: Specialized expert models identify objects, places, actions, named entities, emotions, profanity and hate speech.
How modalities complement each other
The system doesn’t just analyze each modality in isolation. Instead, it can understand the complementary nature of different elements in the same scene. Visual analysis may identify a kitchen setting, audio might capture cooking sounds, and transcript analysis might detect conversation about recipes. Taken together, this creates a complete understanding of a cooking scene.
Extensive internal testing shows that combining modalities significantly improves both precision and coverage compared to single-modality approaches. Using modalities together in a complementary way instead of in isolation is particularly helpful in video content, where different scenes in the same program or show may be best understood through different modalities. A dialogue-heavy scene might be best captured through transcript analysis, while a sports sequence might be better understood through visual cues.
Real-time processing capabilities
Another advantage: using a modular architecture for multimodal AI analysis of video content allows for the selective activation of specific modalities. This makes it possible to enable faster processing for time-sensitive applications like news content or sports and save the full multimodal pipeline for library content that doesn’t require real-time processing.
Beyond basics: from fixed categories to semantic understanding
Traditional contextual targeting systems rely on fixed taxonomies – predefined genres and categories like “sports,” “cooking,” or “drama” that content can be sorted into. While these systems do provide a basic level of organization, they’re fundamentally limited in their ability to capture the rich, nuanced nature of video content. Additionally, they do not generally represent all scene-level opportunities and run the risk of missing high impact advertising opportunities.
A radically different approach
By using semantic embeddings to represent content in a multidimensional space, multimodal AI goes beyond keyword-based understanding to concept-based understanding. Rather than matching on specific words or tags, multimodal systems grasp the underlying meaning and relationships within content.
After segmentation and analysis, each modality’s signals are converted into high-dimensional vector embeddings that represent the semantic meaning of the content – rather than just keywords. Acting to bridge the gap between artificial intelligence and human understanding, embeddings convert any media such as text, images or audio into a format that machines can understand well.
This is what allows the system to understand that a “romantic dinner” may include couples dining, wine glasses, candlelight, and soft background music without a scene being explicitly tagged as such or containing each element. This semantic approach made possible through multimodal AI allows for much more flexible and intuitive contextual matching than previously available.
How semantic understanding works
This more sophisticated understanding allows advertisers to move beyond simple keyword targeting to find contexts that truly resonate with their target audience and brand message, creating more natural and effective advertising experiences.
In a semantic embedding system:
Each scene is represented as a set of vector embeddings in a multi-dimensional space that machines can understand.
Advertisers can query this space using natural language that describes their ideal contextual placement.
The system finds scenes that are semantically similar to the query, even if they don’t match specific keywords.
The flexibility of natural language queries
Perhaps the most powerful aspect of semantic understanding is that it enables intuitive, natural language queries. Advertisers aren’t limited to selecting from a menu of predefined targeting options – they can describe the exact contextual moments they want to target in plain language.
A luxury car brand might target “scenes with winding roads and scenic views”
A financial services company could find “moments where characters discuss future plans”
A beverage brand might seek “social gatherings with friends”
A travel company could target “scenes that inspire wanderlust”
The system can understand these queries and find semantically matching content across the entire content library, regardless of program, genre or platform. This ability to grasp complex concepts holistically is particularly valuable for advertisers seeking to place their messages in highly specific contextual environments that align with their brand positioning and messaging.
From understanding to targeting
After segmentation and the analysis that creates embeddings, every scene will have a rich semantic representation that machines can understand. Using the same models, multimodal, modular AI can understand advertisers’ queries and encode them to ensure semantic matching with content. This enables more nuanced targeting than traditional keyword or genre-based approaches or ACR targeting alone.
Beyond text-based queries, advanced multimodal systems such as Anoki ContextIQ also enable video-to-video as well as audio-to-video search capabilities.
Beyond text-based queries, advanced multimodal systems such as Anoki ContextIQ also enable video-to-video as well as audio-to-video search capabilities.
The Future Of Contextual Intelligence In CTV: 6 Emerging Trends
As multimodal AI continues to evolve, the possibilities for contextual advertising in CTV will expand dramatically. Several emerging trends and technologies point to an exciting future ahead:
1. Real-time content analysis
The speed and efficiency of multimodal AI systems continue to improve, enabling:
Live content analysis: Real-time understanding of live programming
Dynamic ad insertion: Instantaneous contextual matching for live events
Adaptive messaging: Changing creative elements based on the specific context
Second-screen synchronization: Coordinating messaging across devices based on content context
These capabilities will transform how advertisers engage with time-sensitive and live content opportunities.
2. Enhanced emotional intelligence
Next-generation systems will feature more sophisticated understanding of emotional contexts:
Nuanced emotion detection: Moving beyond basic emotional categories to subtle emotional states
Cultural context awareness: Understanding how emotions are expressed differently across cultural contexts
Narrative arc awareness: Recognizing a viewer’s position in a storytelling journey
Emotional journey mapping: Tracking emotional progression throughout content
This emotional intelligence will enable advertisers to align their messages with specific emotional states for maximum impact.
3. Vertical-specific expertise
Future systems will develop specialized expertise for different advertising categories:
Category-specific models: Custom-trained models for verticals like automotive, CPG, or financial services
Specialized object recognition: Fine-tuned detection for category-relevant items and scenarios
Industry-specific safety parameters: Customized brand safety frameworks for different sectors
Regulatory compliance: Built-in guardrails for heavily regulated industries
These specialized capabilities will make contextual targeting even more effective for specific advertising categories.
4. Creative optimization through contextual understanding
The relationship between context and creative will become more synergistic:
Context-optimized creative: Developing advertisements specifically designed for certain contexts
Dynamic creative adjustment: Modifying creative elements based on the specific contextual placement
Contextual creative testing: Understanding which creative approaches work best in different contexts
AI-generated variations: Creating context-specific versions of base creative concepts
This evolution will blur the line between targeting and creative development, creating more cohesive advertising experiences.
5. Integration with other advanced technologies
Multimodal contextual understanding will combine with other emerging technologies:
Shoppable moments: Identifying contexts ideal for commerce integration
Interactive overlays: Contextually triggered interactive elements
Attention measurement: Combining contextual data with attention metrics
Cross-device storytelling: Coordinating messaging across screens based on viewing context
These integrations will create new possibilities for engaging viewers in innovative ways.
6. A privacy-first future
As the advertising world continues to shift away from personal data dependence, contextual intelligence will become increasingly central:
First-party data enhancement: Combining contextual signals with permissioned first-party data
Privacy-preserving targeting: Reaching valuable audiences without personal data
Interest-based grouping: Identifying content that appeals to specific interest groups
Content affinity: Understanding viewing preferences without tracking individuals
This privacy-centered approach positions contextual targeting as a future-proof solution in an evolving regulatory landscape.
Conclusion: The Multimodal Advantage
Multimodal AI represents a transformative advancement in how advertisers can understand and leverage context in CTV advertising. By analyzing content across multiple dimensions simultaneously, these systems achieve a human-like understanding that enables more precise, relevant, and effective ad placements.
Advanced modular architectures that combine multiple expert models offer significant advantages over monolithic approaches. With the ability to customize, update, and adapt individual components without retraining the entire system, these architectures bring AI from “the lab to the field” - making sophisticated contextual intelligence practical and accessible for real-world advertising applications.
The advantages of this multimodal approach are clear:
Precision: Scene-level understanding enables targeting of specific moments rather than broad categories
Comprehension: Multimodal analysis captures the full context of content rather than isolated signals
Flexibility: Semantic embeddings allow for intuitive, natural language targeting
Safety: Nuanced understanding of content enables sophisticated brand safety approaches
Privacy: Contextual targeting functions effectively without relying on personal data
Performance: More relevant ad placements drive measurable improvements in campaign results
Adaptability: Modular architecture enables continuous improvement and customization
Versatility: Multiple search modalities, including video-to-video matching
As the CTV landscape continues to evolve, with viewership fragmenting across platforms and privacy regulations limiting personal data use, multimodal contextual intelligence offers a compelling path forward for advertisers seeking to connect with their audiences in meaningful ways.
By embracing this technology, advertisers can move beyond the limitations of traditional targeting approaches to create more relevant, engaging, and effective advertising experiences – benefiting viewers, advertisers and publishers alike.