TL;DR
Most brands have no system for showing up in AI-generated answers, and the window to build one is narrowing fast.
A growing set of tools helps brands monitor and optimize their owned content for AI citation, but none of them were built for creator-driven strategies.
Later’s creator AEO expertise closes the gap that every other tool in this space leaves open.
Table of Contents
Picture a consumer who wants to know which protein powder a dietitian actually recommends, or which running shoe holds up on wet trails, or what brand of medicine a pediatric nurse trusts for her own kids. Two years ago, that person typed a query into Google and clicked through a list of results. Today, they’re asking ChatGPT, Perplexity, or Gemini and trusting whatever answer comes back.
According to a December 2025 SEMrush study of U.S. consumers who use AI tools, 55% use AI specifically for product research at least weekly, and one in two has made a purchase after using AI for research. Brand marketers already know their customers are asking AI about them. Now they just need to make sure AI is giving consumers the right answers.
That’s the problem Answer Engine Optimization, or AEO (also referred to as GEO or AI search), is designed to solve. A new category of platforms has emerged to help brands build this infrastructure, and the tools have gotten good at what they do. But there’s a gap that every one of them leaves open, one that matters especially for brands whose marketing runs through creators. This guide walks through what the AEO landscape looks like today, how real brands are using these tools, and where creator marketing fits into the infrastructure picture.
The AEO infrastructure problem most brands haven't solved yet
Here’s the structural challenge: according to McKinsey, brand-owned sites make up just 5 to 10 percent of the sources AI search actually references. The other 90 to 95 percent comes from third-party content, including product reviews, community discussions, YouTube videos, and creator posts.
At a recent CMO conference, Lyle Stevens, Later’s co-founder and Chief Strategy Officer, put it plainly: “Most AEO investment is pointed in the wrong direction. Most brands already optimize what they own, but what they don’t realize is that nearly all of what AI says about you comes from third-party sources.”
Most marketing teams have two separate functions that should be working together on this: a creator or influencer team managing campaigns, and a digital or SEO team managing technical optimization. In practice, they rarely coordinate, and neither is fully configured to build AI brand visibility on its own.
The three types of content AI engines actually trust:
Brand-owned content (knowledge): Structured, original content that gives AI models an accurate picture of who you are and what you offer. This is the foundation.
Creator content (validation): Indexable content from industry voices that AI interprets as expert endorsement. Think long-form YouTube, Substack, and niche blogs.
Community content (consensus): Authentic discussion in the spaces AI cites most: Reddit, forums, and product reviews. When your brand shows up here, AI reads it as market consensus.
Most AI search tools address the first layer. Very few touch the second or third.
What the current AI search tool landscape can (and can't) tell you
A clear set of platforms has emerged to help enterprise brands understand their AI search visibility. Each one surfaces a different piece of the picture. Taken together, they show how much signal is now available to brands willing to look, and where the landscape still leaves a meaningful gap unaddressed.
Profound: deep multi-engine monitoring across 10+ AI platforms
Profound is the most data-intensive monitoring platform in the category, tracking brand mentions, citation patterns, and AI search visibility across more than 10 generative AI engines. For enterprise marketing teams that need to understand where they stand before deciding what to do, it’s the right starting point.
The platform is enterprise-grade in both capability and price, and getting the most out of it requires an internal expert who owns AEO as a dedicated function.
AirOps: connecting AI search gaps to owned content production
AirOps connects AI visibility monitoring to content production. Its Opportunities Engine identifies gaps where competitors are showing up in AI answers, then feeds those directly into a workflow where teams can create or refresh content across hundreds of pages simultaneously, publishing through integrations to more than seven CMS platforms.
One important note: AirOps currently tracks only three AI engines (ChatGPT, Gemini, and Microsoft Copilot), which is a meaningful gap given the breadth of platforms like Perplexity and Claude in enterprise workflows.
SEMrush: extending traditional SEO intelligence into AI search
SEMrush’s Enterprise AI Optimization is the right tool for teams already doing serious SEO work who want to extend that discipline into AI search, tracking share of voice, sentiment, and prompt visibility across platforms. It’s a natural evolution of the SEO toolkit rather than a separate system to learn.
For teams already using SEMrush for SEO and keyword research, their AI Visibility Toolkit add-on covers ChatGPT, Gemini, Claude, and Perplexity. It surfaces where you stand in AI responses but doesn’t connect those insights to content creation or publishing workflows. Think of it as a visibility gauge rather than an engine.
Evertune: understanding what AI models believe about your brand
Founded by Trade Desk veterans, Evertune is the only platform in this group that distinguishes between a model’s foundational knowledge about a brand (what AI inherently knows from its training data) and real-time consumer app responses. That distinction matters for brands doing remediation work: if AI is mischaracterizing your product or attributing the wrong claims to your brand, Evertune helps you see that before you optimize.
Its prompt library draws from a proprietary panel of 25 million real users, so the queries being tracked reflect actual consumer behavior rather than marketer assumptions. In 2026, Evertune also expanded into programmatic advertising through partnerships with Index Exchange and The Trade Desk.
Scrunch AI: full attribution across nine AI engines with GA4 integration
Scrunch tracks nine AI engines natively and covers monitoring, prompt prioritization, and site optimization recommendations in one platform. It’s the strongest choice for teams that need full-spectrum citation visibility and want to understand which content fixes will actually move their AI search presence.
Its GA4 integration is also a standout: Scrunch shows actual AI referral traffic broken down by engine, which closes the attribution loop that performance-minded teams need and that most AEO platforms don't offer. Its Core plan covers four AI models and a baseline set of tracked prompts, with custom Enterprise plan options for brands that need broader model coverage, advanced features, API access, and dedicated support.
Later: the creator and community layer no monitoring platform can provide
The platforms above all solve for the monitoring and optimization of owned content: tracking where your brand shows up in AI-generated answers, identifying gaps, and improving the content your brand controls. That work is valuable, and brands should be doing it. Later’s role is different.
We sit on top of that infrastructure as the activation layer for the 90 to 95 percent of AI citations that owned content optimization can’t reach. In practice, that means two things:
Monitoring and insights: Share of Model measurement, citation and mention tracking across the major LLMs, prompt and keyword tracking, competitor benchmarking, and sentiment and accuracy monitoring so brands can see when AI is saying something wrong or unfavorable about them.
Creator strategy and execution: We turn those insights into a creator activation plan and execute it. A platform can show you the gap. Later closes it through the creators and communities AI actually trusts.
Creator AEO can run within an existing creator program, or as a standalone program for brands starting from scratch. Either way, the starting point is the same: identifying the prompts buyers are asking AI in your category, building those into creator briefs upfront, and tracking Share of Model lift as the content goes live.
Later EdgeAI predictive intelligence, trained on $2.9B in creator-driven purchases and 20M+ creators analyzed, identifies which creators, platforms, and formats will increase Share of Model before a brand spends. That’s the part no GEO monitoring platform can provide.
How to build AI search visibility through creator content
Creator AEO layers on top of an existing creator program rather than replacing it. The creator work still happens as it normally would: partnerships, content production, and platform distribution all continue, though the specific choices within each may shift based on what the AEO findings show.
Here’s how it works:
Step 1: Prompt identification
We map the specific questions your customers are asking AI engines in your category: buying intent queries, competitor comparisons, product-specific questions, and broader category prompts. These become the foundation of every creator brief.
Step 2: Creator selection
This is where a creator AEO strategy parts ways with a standard influencer program. Follower count is nearly irrelevant. What matters is:
Content history: LLMs index archives, not just recent posts
Category authority: is this creator already trusted in your space?
Audience alignment with your core consumer
A track record of engagement on educational or informational content specifically
A mid-tier creator who has spent three years making expert content in your category will move AI citations in a way that a high-follower generalist account won’t.
Step 3: Activation
Creators produce content structured around the target prompts, across the platforms AI engines actually cite: long-form YouTube, Reddit, Substack, and niche community spaces. Each piece of content is designed to be indexed, trusted, and cited.
Step 4: Measurement and optimization
Later EdgeAI tracks Share of Model lift over time, alongside mention rate and citation rate across the major AI engines. Every campaign informs the next one, because the intelligence compounds the same way the content does.
Where Later’s creator AEO fits in the enterprise AI search stack
The platforms reviewed here can tell you where your brand shows up, where competitors are pulling ahead, and which content on your own site is underperforming. What none of them can do is reach the 90 to 95 percent of AI citations that come from creator content, community discussions, and third-party sources. Creator AEO is the bridge between what SEO already knows and the content layer that makes up the majority of what AI search actually references, the part that creators are well-positioned to reach.
In practice, working with Later often means collaborating alongside both social and SEO teams to align creator activations around shared brand goals. SEO already knows which keywords and prompts buyers are searching. Later builds creator briefs and activations designed to win those same prompts in AI-generated answers. Both teams work toward shared goals: building trust through third-party content and growing Share of Model over time.
The brands that figure this out first will build a compounding lead. AI models are always retraining, and if your brand isn’t in the content ecosystem when the next knowledge base window closes, your competitors own that space until the next cycle.
McKinsey projects $750 billion in US revenue will flow through AI-powered search by 2028. The infrastructure to capture your share of that is being built right now. The question is whether your creator program is part of it.
More than half of consumers now use AI search as part of their decision-making process. Talk to a strategist today to learn how Later’s creator AEO can become part of your AI visibility infrastructure.
