Optimizely, the AI platform for marketing, announced a family of purpose-built, post-trained AI models built specifically for marketing work. In early testing, these models were able to achieve a 10x greater cost efficiency compared to state-of-the-art LLMs.
Frontier models are built to be generalists, capable of a wide range of tasks, but that breadth comes at a cost. Marketing tasks are nuanced and domain specific, so a general model carries more parameters, pulls in extra context, and spends tokens it never needs. Optimizely’s approach strips away the excess from the state-of-the-art models, leaving models built specifically for marketing tasks.
Marketers often ask the same general-purpose AI models to do a variety of different marketing jobs, from writing campaign copy to analyzing experiments and interpreting customer behavior. But knowing how to perform a task is different from understanding the brand behind it. Optimizely’s global study of more than 2,000 B2B marketing leaders found that 53% say current AI tools can capture the facts of a brand but struggle to capture the emotional resonance that helps it connect with audiences. Optimizely’s models were built around a different premise: AI can take on more of the work without stripping away the context and understanding that make marketing distinctive.
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“The initial results of our AI Lab have surpassed our expectations,” said Imran Yousuf, Head of AI Engineering at Optimizely. “We believe post-training models is the long-term solution to the challenging cost vs. quality debate. Our purpose-built family of models is built to get work done fast and cost-effectively, without sacrificing quality. Customers shouldn’t have to think about which model to use, when, or how. They can just trust that our agent platform will apply the right model for the right use case, so their time and their budget go toward the work that actually grows the business.”
Optimizely is also introducing Mark-Bench, an open source benchmark built to evaluate AI performance for the marketing domain specifically. The company’s intent is for marketers, researchers, and other AI providers to test their own models and agentic harnesses against, giving the industry a shared, objective way to measure both cost and performance on marketing-specific work. Mark-Bench tests models directly against 285 tasks spanning 15 marketing functions, and over 6,000 criteria including writing a press release, creating a social post, and writing email copy.
Optimizely’s models draw on Mark-IQ, the Agent Platform’s data layer, which is built on each organization’s own context, including experimentation history and web analytics, so every model has the brand’s context without a marketer having to rebuild it with every prompt. As evaluated by Mark-Bench’s all-pass rate on default configurations, Optimizely Agent Platform scored 67% compared to 60% for Claude Code at 2x lower cost.
“The problem with generic harnesses is that they’re good for general use and productivity but very inefficient when it comes to domain specific work,” said Shafqat Islam, President of Optimizely. “Marketing is a unique domain that requires agent harnesses to be specialized. By delivering frontier quality at lower costs, our customers finally have a way to scale agentic marketing to meet real-world demands rather than getting stuck in pilot purgatory.”
SOURCE: PRNewswire