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Business Problem
Scaling B2B content marketing while maintaining personalization is costly and resource-intensive. Existing AI solutions assist marketers but still rely heavily on their expertise, limiting automation for companies without dedicated teams.
Our Vision
Autonomous content creation that produces prospect-specific, data-driven messaging from the ground up—replacing traditional content workflows and eliminating the need for expert guidance.
How It Works
AI agents use (publicly) available data on a prospect’s people, operations, and customers to craft personalized content.
Content is inherently personalized from inception, not just refined post-creation.
AI adapts dynamically based on a prospect’s reactions and funnel position.
Key Benefits
Higher engagement and trust through content that feels uniquely relevant.
Scalable B2B content marketing—without in-house marketing expertise.
Personalized content marketing accessible to more companies.
AI Challenges
The biggest challenge is problem-solving—enabling AI to generate relevant, high-value content from prospect-specific data while ensuring safety, adaptability, and contextual accuracy.
Business problem
"Help before selling" is the core principle of Content Marketing. By delivering useful, educational, or inspirational content, companies build trust, establish themselves as a reliable resource in their industry, and generate demand for their products and services.
In the B2B space, content creation serves as a primary promotional tool. However, scaling content marketing in B2B presents unique challenges. There is an inherent tension between reaching a large number of prospects and providing high-quality, personalized help to them.
Traditionally, this trade-off is managed by specialized marketing teams that create content for different prospect segments. Yet, the high costs of this approach often outweigh immediate benefits, making it prohibitive for many companies, especially startups.
While AI-powered solutions promise content personalization, they still rely on marketing experts to guide the process. These tools are designed to assist marketing teams, not replace them, leaving companies without dedicated teams at a disadvantage.
To truly democratize B2B content marketing, we need an approach that delivers personalization without relying on in-house marketing expertise.
Approach
We move away from broad, generic materials—like case studies, white papers, and "ultimate guides"—toward dynamic, data-driven content tailored to each individual prospect. More importantly, we shift AI’s role from assisting marketers in content workflows to autonomously generating prospect-specific content.
By leveraging publicly available data on a prospect’s people, operations, and customers, AI agents can craft marketing content that reflects a precise, contextual understanding of the prospect's business. This not only makes the content more relevant, but also demonstrates a commitment to the prospect’s success—well before any sales conversation begins.
We focus on content that emerges directly from data, making it inherently personalized from the start. This stands in sharp contrast to today’s top-down approaches, where pre-written content is later refined to fit different audiences. Our bottom-up method embeds personalization into the content itself—not as an add-on, but as a foundation.
Beyond personalization, our approach enables content to adapt dynamically based on a prospect’s reactions and position in the marketing funnel. As the goal shifts from building awareness to nurturing consideration to driving conversions, AI continuously refines content to align with the prospect’s journey and feedback.
This bottom-up, adaptive approach fosters engagement and trust while making scalable content marketing accessible to companies without dedicated marketing teams.
AI challenges
Developing AI agents for B2B content marketing involves navigating complex, uncharted territory. Beyond the fundamental challenge of providing enough context for the agent to operate effectively, several challenges must be addressed:
Data access—the agent needs to sift through relevant datasets to create personalized content. While approaches like Retrieval-Augmented Generation (RAG) are promising, this challenge must be researched from the ground up to avoid unnecessary complexity and ensure efficiency.
Feedback Loops—the agent must adapt based on how prospects react to its content. Designing effective feedback mechanisms—such as tracking engagement patterns, sentiment, and behavioral signals—is essential to ensure future outputs align with prospect needs and business goals.
Safety—the agent must adhere to strict ethical guidelines and industry standards to prevent misleading, biased, or harmful content. Maintaining trust and credibility is critical for adoption in professional settings.
While these challenges are significant, the most complex hurdle is problem-solving. Within the constraints of safety rules, feedback, and context, the agent must transform prospect-specific data into relevant, high-value content that effectively drives demand.
This requires advanced problem-solving capabilities during inference—an area where current AI models still struggle. While chain-of-thought techniques and reinforcement learning are improving AI’s ability to reason, we must determine through research and experimentation whether these advancements alone are sufficient or if task-specific training will be necessary to fully overcome this challenge.
Open roles
There are currently no open roles for this initiative.
Follow us on LinkedIn for updates.
Partnerships
This initiative is conducted in partnership with:
Birdie, a leading provider of customer feedback analytics.
Media Hero, an AI startup that successfully optimized R$3 billion in marketing budgets within its first year of operation.
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