How Cognitiv is Bringing Deep Learning to Real-Time Programmatic Curation

As buyers seek smarter ways to activate premium inventory, real-time bidstream data is creating new opportunities to drive performance and unlock greater value. Cognitiv, a leader in deep learning-powered advertising, has embraced this approach, integrating its AI models with Magnite’s RTD offering. Zach Pucci, VP of Enterprise Sales at Magnite, sat down with Jana Jakovljevic, SVP of Partnerships at Cognitiv, to discuss why real-time data has become an important part of their strategy, how RTD is helping them deliver better outcomes for advertisers, and where they see the opportunity going next.

The industry is moving beyond traditional identity-based targeting. How is access to real-time bidstream signals improving the way we evaluate and package premium supply?

Identity has always answered one part of the equation: who. Who is likely to be interested in a product, who falls into a particular demographic, or who has shown intent in the past. But driving performance is about more than identifying the right person. Context tells you when and where an ad is most likely to influence a decision.

That’s why real-time bidstream signals are so valuable. Instead of relying on audience segments built days or weeks ago, Cognitiv’s deep learning models evaluate every impression in under 10 milliseconds, assessing both the user and the context surrounding that impression to predict the probability of conversion.

Having that intelligence at the moment of the bid means we can assign a more accurate value to each impression, driving better performance for advertisers and increased demand for publishers.

How is Cognitiv using proprietary models and data science to create differentiated value for buyers through real-time data?

Our differentiation isn’t just access to data; it’s what our models have learned from it over the last decade. We’ve invested years in building proprietary deep learning models and embeddings that capture complex relationships between content, consumer engagement, and advertising outcomes. Rather than relying on rigid rules or static audience segments, our models recognize patterns that indicate when an impression is most likely to drive a desired outcome.

Our Deep Learning Advertising Platform continuously learns from campaign performance and adapts to each advertiser’s KPIs in real time. Combined with the compute power of our co-location infrastructure, this enables advertisers to identify and act on high-value opportunities that traditional approaches often miss.

How does Cognitiv’s deep learning technology plug directly into Magnite’s ClearLine solution, and what does that workflow look like from the moment a bid request happens to when a buyer activates a Deal ID?

By plugging Cognitiv’s deep learning directly into Magnite’s ClearLine curation platform, we evaluate supply at the source.

1. An ad request fires: A viewer starts streaming CTV or lands on a publisher’s site, sending an impression opportunity to Magnite.
2. AI scores the moment: Magnite passes live bidstream signals to Cognitiv, where deep learning models analyze the content and context in under 10 milliseconds. Cognitiv’s ContextGPT analyzes deep page sentiment, while AudienceGPT captures real-time consumer intent to predict campaign performance.
3. Inventory gets packaged: Qualifying impressions are dynamically packaged into a custom ClearLine Deal ID.
4. Buyers activate seamlessly: Magnite sends the enriched Deal ID downstream to the buyer’s DSP of choice, clearing the bid through their normal workflow.

Why does Cognitiv do this via a server-to-server integration rather than containerization, and how does Magnite support that S2S integration?

Containerized RTB infrastructure was designed to execute lightweight logic at scale, while deep learning requires significantly more compute to run sophisticated neural network inference within the strict latency requirements of real-time bidding. That’s why we built a server-to-server integration.

Our S2S architecture provides approximately 900% more compute than containerized environments. Magnite’s server-to-server integration gives us direct, low-latency access to the bidstream, allowing our models to score impressions and return decisions within the milliseconds available for an auction.

Looking at the performance buyers have seen from this integration, what benefits are brands experiencing with this deep-learning-powered curation?

Brands are getting more precise targeting, stronger engagement, and better cost efficiency. Real-time curation cuts wasted spend and drives down campaign costs. Our clients saw 31% more cost-efficient CPCs through Magnite DV+ compared to non-real-time curation implementations.

Additionally, in a recent campaign for a leading national insurance brand, Cognitiv’s ContextGPT strategy delivered the highest click-through rate of any tactic tested, with Magnite serving as a key supply partner in the deal.

How does your real-time data help you identify and act on the best opportunities across CTV, display, audio, and mobile through Magnite?

Consumers don’t engage with media in silos, so neither should AI. This means that what someone was reading on mobile, browsing on desktop, or listening to earlier in the day provides valuable context about where they are in their journey.

Cognitiv’s device graph spans more than 250 million U.S. adults, allowing our deep learning models to connect those cross-channel signals and understand the path leading up to an impression. Rather than evaluating CTV, display, audio, or mobile independently, we evaluate them together to determine the probability of a business outcome.

Working with Magnite gives us access to real-time signals across premium omnichannel inventory, enabling our models to identify the moments that matter most.

Looking ahead, how do you see real-time signal activation shaping the future of programmatic curation, and why is collaboration between SSPs and curators becoming increasingly important?

The future of programmatic isn’t about giving buyers more choices—it’s about removing the need to make tradeoffs.

Historically, advertisers layered together audience, contextual, viewability, and brand safety signals in the hope of improving performance, often optimizing one metric at the expense of another.

Deep learning changes that. Our models evaluate hundreds of signals simultaneously to predict the likelihood of a desired outcome, meaning advertisers no longer have to choose between contextually relevant content, high viewability, or the right audience.

Making that possible requires close collaboration between SSPs and AI-powered curators. SSPs provide access to premium inventory and real-time signals, while curators apply the intelligence that determines which opportunities are most valuable. Together, they’re transforming curation from static rules into real-time, outcome-based decisioning.