B2B marketers have long used manual workflows to manage their ad campaigns, an approach that's grown increasingly unsustainable as digital advertising becomes more complex. At the same time, companies of every size are under pressure to generate more precise results from their ad spend. This tension has created demand for tools that can move faster than regular teams effectively can.
Multiply (formerly known as Kalos), a company where Pranav Pawar serves as a founding engineer, is hoping to ease this shift by developing agents that handle campaign execution front to back at a speed traditional models would struggle to match. His work is one example of a transformation underway in the marketing industry, one defined by deeper automation and constant refining.
Why Modern B2B Campaigns Struggle According to Pawar
B2B companies routinely direct tens of thousands of dollars each month toward LinkedIn advertising (currently accounting for 39% of all B2B ad spend), yet the mechanics behind those campaigns remain largely manual. Marketers who should focus on big-picture planification wind up taking care of tasks like audience segmentation, creative generation, bid calibration, and campaign fine-tuning, creating workflow drag at precisely the stage where they should be rolling out campaigns.
Even with solutions that aim to solve this with AI, most address only isolated steps like creating copy or visuals, leaving marketers without a system capable of managing the full campaign lifecycle.
This problem's further compounded by the growing digital channels available. Multi-channel execution introduces conflicting data streams, fragmented testing environments, and longer turnaround times. Fast-growing startups face an additional constraint: limited agility. Without tools that support large-scale parallel experimentation, teams are forced to test only a handful of creative and audience combinations, reducing their statistical confidence and slowing iteration loops.
The result is a high-spend environment where decisions are built on signals that may not be fully complete or even accurate. For engineers like Pranav Pawar, with a background dealing with healthcare and enterprise-grade research, the gap represented a clear technical opportunity: build infrastructure that automates orchestration, not just asset creation, so campaigns can continuously upgrade themselves in real-time without getting constrained by manual workflows.
Building a Framework for AI-Driven Advertising
Multiply was built to automate the full lifecycle of B2B advertising campaigns through coordinated AI agents. Its platform integrates directly with LinkedIn and Google Search advertising infrastructure, connecting to internal company data sources, including CRM records and sales call transcripts, to generate ad copy, creative, and targeting variations that are tested continuously.
The premise is that sales conversations often contain the most accurate explanation of why customers choose one product over another, insights that rarely make their way into marketing campaigns quickly enough. Multiply's software extracts that language and feeds it back into advertising experiments at scale.
Pawar's role involves taking care of both the underlying infrastructure and the front-end experience. As one of the earliest engineers at the company, he works across the stack, transforming early prototypes into a scalable self-serve product. "Our AI agents run hundreds of A/B tests so businesses know exactly what works before scaling spend," Pawar explains.
He views the agents as force multipliers that free teams to think strategically instead of performing routine operational tasks. This notion is a reflection of Pawar's experience with AI in healthcare, which required a human in the loop at all times, even if agents execute much of the analysis.
The Results Emerging from Early Deployments
Early customers using Multiply report meaningful reductions in the time required to launch and refine campaigns. Tasks that once took weeks, from segmentation to creative development, can now be completed in hours, and marketers could run continuous experiments (instead of sticking with a single creative direction) to better understand their intended audience behavior.
Beyond time savings, customers can benefit from improved lead generation metrics driven by the system's ability to compare large numbers of ad combinations. Pawar notes that the agents' capacity to switch up marketing angles and adapt existing campaigns without limits is key to these gains. While a human marketer may test several variations of a campaign, an agent can test dozens in parallel, adjusting continuously as results emerge. "Automation doesn't replace marketers—it lets them focus on strategy," he says.
Because the platform gives companies access to each ongoing campaign, they can always remain in control over how they present their brand voice, have the final word on all creative processes, and be aware of what values they're showcasing to their intended audience.
What the Next Era of Marketing Could Look Like
Pawar approaches the industry not only as an engineer but as someone who's spent years studying how agents can assist humans without ultimately replacing or displacing them. With that experience, he predicts the next generation of advertising tools will essentially be networked systems dealing with complex, multi-channel campaigns without the need for step-by-step human intervention.
In this model, marketers would zero in their work to focus near-exclusively on tasks demanding more human creativity, positioning, and long-range strategy, while the agents would manage execution, optimization, and cross-platform consistency. Pawar believes this transition will allow small and midsize businesses to access capabilities that only large enterprises could have access to. "AI will make marketing adaptive, autonomous, and infinitely scalable," he says.
He also sees more use cases for platforms like these, even going beyond marketing. He eventually expects businesses of all sorts to adopt suites of AI-driven platforms, each one handling specific tasks tailored to the individual needs of their workflows.
Multiply represents one step toward that shift. Through the platform, Pranav Pawar and the team aim to help companies reimagine what campaigns can achieve when experimentation is limitless, data is actionable, and execution becomes a continuous, intelligent process. As he puts it, "I'm very excited to continue building agents that run robust marketing campaigns, learn from audience interactions, adapt strategies dynamically, and drive greater traction for businesses."
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