If your brand isn’t showing up in AI-generated answers, it may already be losing deals before a prospect ever visits your website. That’s the premise behind Yolando, a new competitive intelligence and Generative Engine Optimization (GEO) platform that officially launched today with $8.5 million in total funding from Drive Capital.

Yolando is built for a reality many marketing teams are only beginning to confront: buyers now consult AI tools like ChatGPT, not just Google, to decide which brands even make the shortlist. The platform is designed to help companies understand how they appear in AI-generated responses—and, more importantly, what to do about it.

Why AI Answers Are the New Front Door

The traditional marketing funnel has quietly shifted. Instead of landing on a homepage or clicking a paid ad, prospects are increasingly starting with a conversational prompt: Who’s the best vendor for X? Which company should I trust?

The brands mentioned in those AI-generated answers gain an immediate advantage. Those that aren’t effectively disappear.

This change affects nearly every sector that depends on visibility and trust—from B2B SaaS vendors chasing enterprise deals to law firms, healthcare providers, and financial services companies competing locally. When AI answers a question like “Who’s the best option in my area?”, the response often determines who gets considered at all.

Yolando positions itself squarely at this inflection point, aiming to give marketing teams a way to influence those AI-driven outcomes rather than simply observe them.

Built by Marketers Who Felt the Shift First

Yolando isn’t a theoretical product built in isolation. Founder and CEO Matt Bogoroch also leads BirdseyePost, a fast-growing marketing software company that was already feeling the downstream effects of AI-driven discovery.

As AI tools began influencing which vendors made it onto buyer shortlists, Bogoroch’s team realized something was missing from the market. Existing platforms could measure visibility and sentiment, but they stopped short of helping teams respond in a meaningful, scalable way.

So they built their own.

In production use at BirdseyePost, Yolando reportedly reduced demand-generation friction, improved visibility in AI-generated answers, and accelerated deal velocity. According to the company, inbound leads originating from ChatGPT were worth 20% more in revenue and closed 40% faster than leads from Google search, outbound sales, or referrals.

What started as an internal solution has now been commercialized for other teams navigating the same transition.

“Before a prospect ever visits your website, they’ve already asked ChatGPT who to trust,” Bogoroch said. “If you’re not in that answer, you’re not in the conversation. Yolando makes sure you are.”

How Yolando Tries to Stand Apart in a Crowded GEO Market

The rise of generative search has triggered a rush of new GEO tools, many of which focus heavily on dashboards, rankings, and sentiment scores. Yolando’s pitch is that insight alone isn’t enough.

Instead of stopping at visibility analysis, the platform combines continuous competitor monitoring, strategic recommendations, and on-brand content generation—closing the loop between insight and execution.

The idea is simple: if a competitor gains traction in AI-generated answers, marketers shouldn’t just see it. They should know exactly how to respond.

Yolando’s recommendation engine is built to support that workflow by:

• Tracking new on-site content published by competitors and proposing a strategic response

• Alerting teams when rivals gain visibility for high-intent prompts or search terms

• Flagging drops in brand sentiment or ranking before they impact pipeline

• Identifying content gaps that keep brands out of AI-generated responses

• Generating publish-ready content briefs in one click to speed execution

The emphasis is on speed and clarity. As Bogoroch puts it, many tools “tell you that you’re losing” but leave the fix as an exercise for the reader. Yolando aims to compress weeks of analysis and planning into minutes.

The Technology Under the Hood

At the core of Yolando is a proprietary model trained on millions of webpages to identify the patterns that influence LLM citation and recommendation behavior. Rather than focusing only on traditional SEO signals, the model looks at why specific AI systems choose to mention certain companies over others.

According to Shardul Frey, Co-Founder and CTO, this approach is meant to avoid one of the common pitfalls of AI-generated content: repetition.

“Traditional AI-generated content often just parrots existing sources,” Frey said. “We trained our system to identify patterns that actually drive LLM citation behavior, so every piece of content Yolando generates introduces something genuinely new to the conversation.”

The platform coordinates more than 40 specialized agents that handle research, competitive analysis, fact-checking, and formatting. Real-time performance data feeds back into the system, allowing the model to refine its recommendations as AI behavior evolves.

A Feedback Loop Few Startups Have

One unusual advantage for Yolando is its operating structure. The same team building the platform continues to run and scale BirdseyePost alongside it. Both companies operate under one roof, giving Yolando a built-in proving ground inside a high-growth business facing AI-driven buyer behavior daily.

That feedback loop could matter as generative search continues to change quickly. Instead of reacting months later to industry shifts, Yolando is being stress-tested in real buying cycles as they happen.

Why This Matters for MarTech Leaders

GEO may still sound like a buzzword, but the underlying shift is already underway. AI-generated answers are becoming a new layer of influence—one that sits above websites, ads, and even brand search results.

For marketing leaders, the implication is clear: visibility is no longer just about ranking on Google. It’s about being recognized, trusted, and cited by AI systems that increasingly shape buyer decisions.

Yolando’s launch is a sign that this space is moving from experimentation to execution. As generative engines become a permanent part of the discovery journey, tools that connect insight to action may define the next phase of marketing optimization.

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