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Magnite partners with Anoki to enable scene-level targeting in CTV

Multimodal AI platform integration marks first implementation of ContextIQ technology by supply-side platform.

Luis Rijo

• 6 min read
Anoki AI interface showing scene-level targeting options for CTV campaigns with Nike brand example
Anoki AI interface showing scene-level targeting options for CTV campaigns with Nike brand example

Magnite has integrated Anoki's ContextIQ platform into its SpringServe technology stack, becoming the first supply-side platform to offer scene-level contextual targeting capabilities for connected television advertising. The partnership, announced two days ago on June 5, 2025, introduces multimodal artificial intelligence analysis to CTV campaigns through Magnite's infrastructure.

ContextIQ represents a purpose-built AI engine that analyzes multiple dimensions of video content simultaneously. The technology examines scene composition, emotional sentiment, and brand safety parameters within CTV environments. According to the announcement, this integration enables advertisers to align campaigns with specific content moments rather than traditional demographic or behavioral targeting methods.

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The technical implementation occurs within Magnite SpringServe, where the ContextIQ platform provides real-time analysis of video content. This analysis generates metadata that describes the contextual characteristics of individual scenes, enabling advertisers to purchase inventory based on content adjacency rather than audience characteristics alone. Publishers gain access to granular insights about their content's contextual value, potentially unlocking monetization opportunities previously unavailable through traditional targeting approaches.

Kristen Williams, SVP of Partnerships at Magnite, positioned the integration as addressing optimization challenges across multiple screens. According to Williams, "At Magnite, we've long been focused on building and enabling tools that help our clients optimize across every screen, and this integration with Anoki takes that commitment to the next level." The partnership embeds AI-powered scene analysis directly into Magnite's CTV technology infrastructure.

The ContextIQ technology operates through multimodal analysis, processing visual, auditory, and contextual elements within video content simultaneously. Abbey Thomas, Chief Commercial Officer at Anoki, explained the technical approach: "ContextIQ leverages multimodal AI to capture the full emotional, visual, and auditory context of every scene. That allows publishers and advertisers to unlock more precision, brand safety, and emotional resonance in CTV."

This implementation addresses several technical challenges within CTV advertising. Traditional demographic targeting relies on user identification and behavioral data, which face increasing restrictions due to privacy regulations. Contextual targeting through content analysis provides an alternative approach that focuses on programming characteristics rather than individual viewer identification.

The system generates scene-level metadata that enables inventory categorization based on content attributes. Advertisers can specify targeting parameters related to emotional tone, visual elements, or thematic content within individual scenes. This granular approach contrasts with program-level categorization used in traditional television advertising.

Brand safety considerations represent a critical component of the ContextIQ implementation. The technology assesses content suitability in real-time, enabling advertisers to avoid placements that conflict with brand guidelines. This automated assessment process reduces manual review requirements while maintaining campaign alignment with advertiser objectives.

The technical architecture supports transparency features that provide advertisers with detailed reporting about ad placement context. Campaigns can access information about the specific scenes where advertisements appeared, enabling performance analysis based on contextual factors rather than traditional metrics alone.

Publishers participating in the integration gain access to enhanced inventory monetization capabilities. The system identifies high-value content moments that align with specific advertiser objectives, potentially commanding premium pricing compared to standard inventory. This approach enables content creators to capture additional value from contextually relevant programming.

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A+E Global Media has committed to utilizing the integrated technology across its entertainment portfolio. Roseann Montenes, Head of Audience Innovation & Digital at A+E Global Media, highlighted the potential for enhanced advertiser value: "This integration allows us to marry the power of A+E's best-in-class entertainment portfolio with state-of-the-art contextual tech, enriching viewers' experience with ads far more relevant, resonant, and aligned with the content on screen."

The timing of this announcement coincides with broader industry movement toward contextual advertising solutions. Privacy regulations and the deprecation of third-party cookies have prompted advertisers to explore targeting methods that do not rely on individual user tracking. Contextual analysis provides an alternative that maintains targeting precision while addressing privacy concerns.

CTV advertising represents a rapidly expanding segment within digital advertising, with spending projected to continue growing as audiences migrate from traditional linear television to streaming platforms. The introduction of scene-level targeting capabilities through AI analysis potentially enables more sophisticated campaign optimization within this growing channel.

The integration encompasses multiple technical components beyond basic targeting capabilities. The AI Copilot feature provides campaign planning tools that include brand suitability filters, scene recommendations, inventory forecasts, and preview capabilities. These features enable advertisers to understand potential ad placement contexts before campaign execution.

Contextual intelligence generation occurs through analysis of programming content rather than viewer behavior. This approach aligns with privacy-first advertising strategies while maintaining relevance for target audiences. The system generates metadata describing content characteristics without requiring individual user identification or tracking.

Scene-level analysis enables targeting precision that exceeds traditional program-level categorization. Rather than targeting entire programs or dayparts, advertisers can specify contextual parameters for individual moments within programming. This granular approach potentially improves campaign relevance while reducing waste on unsuitable inventory.

The multimodal AI approach processes multiple data streams simultaneously to generate comprehensive content understanding. Visual analysis examines scene composition, color schemes, and object recognition. Audio analysis processes dialogue, music, and sound effects. Combined analysis generates emotional and thematic categorization for individual scenes.

Performance measurement capabilities enable advertisers to analyze campaign effectiveness based on contextual factors. Rather than traditional demographic performance metrics, campaigns can assess outcomes based on specific contextual targeting parameters. This approach provides insights into content adjacency effectiveness for different advertiser objectives.

The technical implementation maintains real-time processing capabilities necessary for programmatic advertising environments. ContextIQ analysis occurs dynamically as content streams, enabling immediate inventory categorization and targeting activation. This real-time processing ensures that contextual metadata remains current and accurate for advertising decisioning.

CMI Media Group has recognized the potential pharmaceutical industry applications for the technology. Justin Freid, Chief Media & Innovation Officer at CMI Media Group, noted privacy advantages: "This partnership enables us to deliver pharmaceutical messaging at the most impactful moments, while maintaining the privacy standards our clients need."

The integration represents a significant technical advancement for programmatic CTV advertising. Traditional contextual targeting relied on program-level metadata or basic content categorization. Scene-level analysis through multimodal AI provides granular insights that enable more sophisticated targeting strategies while maintaining scale requirements for programmatic operations.

Industry experts anticipate that contextual targeting will become increasingly important as privacy regulations continue evolving. The elimination of third-party cookies and restrictions on device-level tracking create demand for targeting approaches that do not depend on individual user identification. Content-based targeting provides a viable alternative that maintains campaign effectiveness while addressing regulatory requirements.

The technical architecture supports integration with existing advertising technology infrastructure. Magnite SpringServe provides the foundation for ContextIQ deployment, enabling seamless adoption by current platform users. This integration approach reduces implementation complexity while providing immediate access to advanced contextual targeting capabilities.

Publisher monetization benefits extend beyond premium inventory pricing. The system enables content creators to understand the contextual value of their programming through detailed analytics and insights. This understanding can inform content strategy decisions while maximizing advertising revenue potential through optimized inventory packaging.

Competitive differentiation emerges through the exclusive nature of the ContextIQ integration within Magnite SpringServe. As the first supply-side platform to implement this technology, Magnite gains access to unique inventory categorization capabilities that distinguish its offering from alternative platforms. This technical advantage potentially influences advertiser platform selection decisions.

The announcement indicates broader industry trends toward AI-powered advertising solutions. Machine learning and artificial intelligence technologies enable increasingly sophisticated content analysis and targeting capabilities. The implementation of multimodal AI for contextual advertising represents an early example of these technologies' application to programmatic advertising challenges.

Future development possibilities include enhanced content categorization capabilities and expanded targeting parameters. The foundational technology enables continuous improvement through machine learning optimization and expanded training data. Additional contextual dimensions could be incorporated as the AI models evolve and improve.

Why this matters

This development represents a fundamental shift toward content-based targeting that addresses growing privacy concerns while maintaining campaign precision. Marketers can now align advertisements with specific emotional and contextual moments rather than relying solely on audience demographics or behavioral data. The technology enables pharmaceutical companies, automotive brands, and other advertisers to reach audiences during contextually relevant programming moments while meeting strict privacy requirements. As third-party cookie deprecation accelerates, contextual targeting through AI analysis provides a scalable alternative that preserves advertising effectiveness without individual user tracking.

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