Those recommendations can then be turned into automated rules that ad buyers execute directly in platforms like Meta, Google, and TikTok, with every action logged for review.
The system currently manages more than $100 million in annual ad spend through its analytics platform and processed $20 million during the first four months of AI-agent pilots, according to Ustinov.
From insight to execution
According to Ustinov, the company’s differentiation lies not in generative AI alone, but in the context behind it.
“If you just give a general model your data, it will give generic answers,” he said. “We analyze the entire business funnel—sales, revenue, benchmarks—so the agent already understands the specifics before it responds.”
The company is targeting a segment it believes is underserved by existing tools: consumer software and service businesses that operate outside traditional web-based attribution models. Many of those companies rely on CRMs or billing systems like Stripe, making standard pixel-based optimization less effective.
Early adopters include TripleTen, an international edtech platform that runs campaigns across Meta, Google, TikTok, and YouTube. According to the company, Plurio reduced campaign analysis time from more than an hour to roughly 10 to 15 minutes and saved about 20 hours per month.
The company currently has fewer than 100 paying customers across its analytics and AI products and is rolling the agent out to its existing base. Pricing is expected to be tied to a percentage of managed ad spend.
Plurio’s broader bet is that performance marketing is moving toward closed-loop automation, where systems don’t just report what happened but continuously optimize outcomes.
As AI capabilities expand, the real competition may not be dashboards versus dashboards, but people versus systems that never stop watching the numbers.
“Our goal isn’t to add another tool,” Ustinov said. “It’s to give teams their time back and make fast decision-making possible.”
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