MIAMI — IAS launched Total TV to address advertiser reluctance to shift linear television budgets toward connected TV due to transparency gaps, providing show-level and channel-level verification through integrations with Amazon Prime, Paramount, Disney, and NBCUniversal.
“If you ask brands, why won’t you shift more linear TV dollars over into CTV? They often say it’s because I want the same transparency I get in linear TV. I want to know by channel, by show,” Utzschneider said.
The solution enables CTV platforms to offer linear-like transparency that brands demand before reallocating traditional television investments toward streaming environments.
IAS Agent provides real-time analytics
Beyond AI-powered classification technology that drives most IAS products, the company deployed IAS Agent as customer-facing tool for real-time analytics and performance monitoring of media quality solution effectiveness.
“IAS Agent is a tool that we provide to our customers where they can go and see real time analytics, performance, how our media quality solutions are driving greater ROI,” Utzschneider said. “The tool is live. It’s accessible to brands today.”
This represents practical AI application rather than theoretical capability, putting agentic functionality directly in customer hands for immediate business value.
Multimedia classification creates differentiation
IAS’s competitive advantage stems from sophisticated multimedia classification technology that analyzes video, image, audio, and text content in real-time across open web, demand-side platforms, and social platforms with AI-fueled accuracy.
“We are able to classify real time video, image, audio and text. It is fueled by AI and the accuracy rates we’re seeing at the speed that we’re seeing and maintaining the cost, it’s off the charts,” Utzschneider said.
The technology evolution continues through large language model integration that enhances classification capabilities for brand safety and suitability verification across diverse content formats.
“The brands are really leaning into our classification capabilities,” Utzschneider said, adding,” and we can’t wait to share how our models continue to evolve and how we continue to leverage LLMs.”
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