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The San Francisco startup emerges from stealth with Mayfield’s backing and a narrative that treats advertising creativity as a continuous learning cycle, not a quarterly result.
Every B2B marketing team knows the problem. A campaign is launched, the creative is new, the targeting seems right, and then it slowly starts to die. The public disconnects. Click rates drop.
The agency returns for a creative refresh and the cycle begins again. Matt Jayson calls this “decadent ads” and it is, according to his explanation, a structural flaw in how digital advertising is built: campaigns that begin to lose effectiveness the moment they are published, because the feedback loop between what customers really say and what the ads really say is too slow.
On Wednesday, the launch Multiply emerged quietly with $9.5 million in funding to address that problem. The round was led by Mayfield, with participation from Sorenson Capital, Instacart co-founder Max Mullen, and Josh Woodward. Vice President of Google and Gemini Labs, the executive credited with creating NotebookLM and overseeing Google’s flagship AI application.
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Executives from HubSpot, Braze, Brex, Sierra and Common Room also joined the round.
Multiply’s argument is that modern B2B companies already have the data they need to run much better advertising, they just aren’t using it. Sales call recordings, CRM pipelines, and closed deal data contain accurate information about why customers actually buy.
Multiply’s system connects directly to those sources and uses a set of artificial intelligence agents to translate them into continually improving advertising campaigns on Google Search and LinkedIn.
Hundreds of structured experiments run in parallel each week, testing messages, audiences and creatives, with winners scaled and losers automatically trimmed.
The agent architecture is divided into five components. A Customer Insights agent extracts language from sales calls to personalize ad copy. An ICP agent analyzes closed deals to adjust targeting to the audience.
A Quality Score agent adjusts keyword alignment and text for Google ranking signals. A creative design agent updates images on a weekly cycle. An A/B testing agent runs the experiments and identifies what works.
Human media buyers sit on top of all this, providing brand oversight and compliance review, the “hybrid” in what Multiply describes as a hybrid model of AI, plus human agency.
Jayson, who previously worked at Google in user acquisition and then at Brex as Head of Product for Core Experiences, describes the gap the company is trying to close: The insights that drive deals, the targeted objections, the comparisons to competitors, the language that truly resonates, rarely make their way back into ad campaigns quickly enough.
Its co-founder and CTO, Ashish Warty, spent five years as senior vice president of product and engineering at HackerOne and held senior engineering roles at Airship and Dropbox.
“Modern businesses already have all the data they need to create radically better ads.” Jayson said in a statement. “Sales conversations, CRM systems, and process outcomes reveal exactly why customers buy, but those insights rarely make their way into advertising campaigns quickly enough.”
The timing is deliberate in another sense. Multiply’s infrastructure, the company says, is already positioned for ChatGPT advertising, a format that OpenAI has indicated it intends to launch but has not yet done so at scale.
The argument is that the same campaign learning systems built for search and social can be extended to conversational and AI-based ad formats as they emerge. This is a forward-looking statement that will depend entirely on how those platforms eventually structure their advertising products.
“A major shift is underway in the $50 billion B2B advertising market.” Patrick Salyer, a partner at Mayfield and a member of Multiply’s board of directors, said in a statement. “Service as software is redefining how businesses grow and Multiply has created the first artificial intelligence model for B2B advertising.”
The $50 billion market figure comes from Mayfield’s own framework and has not been compared to independent market data.
Multiply is, in essence, making a structural argument about where the advertising agency model fails: not in creative execution, but in the speed of the feedback loop.
Whether a $9.5 million AI stack can fix that problem faster than the headlines adapt is the question its pipeline metrics presumably aim to answer.
