Brand Mentions Are the New Currency of AI Search and Here’s How to Earn Them

In this week’s episode of Voices of Search, we spoke with Rahul Jain, CEO and co-founder of Noble, an AI search platform that helps brands get mentioned in the sources cited by large language models. Noble reached $1 million in ARR in just four months after pivoting into AI search, not because they had a deep SEO background, but because they identified a problem every marketer was already trying to solve.

Rahul came to this space from healthcare enterprise sales, not SEO. He and his co-founder, Josh, started Noble two years ago with a completely different idea—a peer recommendation layer that let B2B buyers see which of their colleagues were already using a product before making a purchase decision. The pivot into AI search happened organically from here, as every marketer they spoke to had the same frustration: ranking on page one of Google but not showing up in ChatGPT or AI Overviews at all. 

That gap, and what it actually takes to close it, became the foundation of what Noble does today.

Key Takeaways From This Episode:

• Brand mentions across the web are the greatest correlating factor to appearing in AI Overviews, according to Ahrefs’ analysis of 75,000 brands.
• Being a cited source in an LLM response is less valuable than being mentioned in the answer itself. Most users never click citations. They read the answer and move on.
• The citation gap between Google rankings and LLM answers is real and growing. Many brands ranking on page one of Google are completely invisible in AI responses.
• Traditional PR, affiliate networks, and PR newswires are largely ineffective for LLM visibility because the sources they target are rarely cited by models.
• Visibility: how often your brand shows up in the answers for prompts you care about. This is the right metric to track today. Transactions and demos booked directly from LLMs are where measurement is heading.
• This is the early-mover moment. Smaller brands can get ahead of larger incumbents in AI search right now, the same way early-stage companies could outmaneuver established players in Google’s early days.

Understanding the Citation Gap and Why It Matters

To understand what Noble does, it helps to understand the problem they’re solving: the citation gap between where a brand ranks in Google and where it shows up in AI-generated answers.

When someone types a prompt into ChatGPT or triggers an AI Overview in Google, the model isn’t just pulling from a ranked list of pages. It’s synthesizing information from a set of sources, typically 10 to 15 citations per prompt, that it deems most relevant to answering the question. A brand can rank on page one of Google and still be entirely absent from that source set. That’s the gap.

“A lot of them were ranking on the first page of Google, but they weren’t showing up in the answers for the prompts that they cared about,” Rahul said. “We kept hearing this pain point over and over.”

Noble’s hypothesis, now backed by data, is that getting your brand mentioned in the sources being cited by LLMs for specific prompts directly influences whether your brand shows up in the answer. “We have seen that when you get your brand mentioned in the sources being cited by the LLMs for those search prompts, you then start showing up in the answers as well,” Rahul explained.

Mentions Over Links: Why the Copy Matters More Than the Click

One of the more counterintuitive arguments Rahul makes is that being a cited source (the clickable link shown at the bottom of an AI response) is actually less valuable than being mentioned in the answer itself.

The reasoning comes down to user behavior. The vast majority of people using ChatGPT or Google’s AI Overview read the answer presented to them and move on. They don’t dig into citations. They don’t click through. “You just want to show up in the answer,” Rahul said. “It’s very clear from user behavior that you want to show up in the answer.”

This has a direct implication for how brands should think about AI search strategy. Traffic to the website is no longer the primary measure of success. What matters is whether your brand’s name, product, or positioning appears in the synthesized response that a prospect actually reads.

It also reframes the relationship between backlinks and brand mentions. Backlinks are valuable for Google rankings, but in an LLM context, the link itself matters less than the copy surrounding it. “These large language models care about the copy that’s in these articles so they can provide the best answer,” Rahul said. The way your product is described in a third-party source is what influences whether and how you show up in a response.

Why Traditional PR Misses the Mark for LLM Visibility

One of the clearest frameworks Rahul offered was a breakdown of why existing off-site strategies, traditional PR, affiliate networks, and PR newswires largely fail to move the needle in AI search.

• Traditional PR focuses on tier-one publications, like the New York Times, Wall Street Journal, and Forbes. These are great from a brand credibility standpoint, but they’re rarely cited by LLMs. Many of them block crawlers entirely, and the ones that don’t typically aren’t covering B2B products with the specificity that makes them useful for commercial prompts.
• Affiliate networks were an early hypothesis that didn’t pan out. Even if you have access to thousands of authors and publishers, if those publishers aren’t already being cited by LLMs for the prompts you care about, getting mentioned there won’t help.
• PR newswires like Stacker can be effective for syndication and getting into citations as links, but that’s a different goal than getting mentioned in the answer itself.

What LLMs actually cite, Rahul explained, looks more like longtail PR: blogs, listicles, and niche articles you’ve probably never heard of. “It’s these blogs and listicles and articles written, and there are sites that you probably have never heard of before—but that’s where you really need to focus.”

Noble’s approach is to run the prompts a brand wants to show up for, identify which sources are being cited, find the authors and publishers behind those sources, and build relationships to get the brand mentioned in exactly the right places. “I kind of like to think about Noble as almost like a longtail PR,” Rahul said.

The Early-Mover Opportunity

One of the more energizing parts of the conversation was Rahul’s argument that AI search represents the same kind of early-mover window that Google SEO offered in the early 2000s, and that smaller brands are better positioned to capitalize on it than larger, enterprise incumbents.

“Imagine you had the ability to do SEO in the early 2000s when Google was just launching,” Rahul said. “That’s the opportunity today.” A smaller brand can start showing up for prompts that larger competitors can’t even rank for on Google, simply by moving faster and being more intentional about off-site mentions.

The reason incumbents are slow isn’t complacency, but complexity. “We’re talking to a lot of those bigger brands, those Fortune 500 companies, and they’re just at the visibility tracking stage. They’re just trying to figure out how often they show up. They’re not at the action and intervention stage yet.”

For early-stage and mid-market companies willing to make a calculated bet now, that lag is an opening. The playbook will eventually be commoditized. The brands that build a presence in LLMs now will have a head start that compounds.

What to Measure and How to Think About Attribution

Measurement in AI search is genuinely hard right now, and Rahul is candid about it. The analytics most teams are looking at are misleading—particularly traffic-based attribution—which misses Google’s AI Overview entirely since users rarely click through from it.

His recommendation for brands trying to understand their AI visibility today:

• Visibility score: Of the prompts you care about, what percentage include your brand in the answer? This is the primary metric Noble tracks for clients.
• Share of voice: How does your visibility compare to competitors for the same prompts?
• Self-reported attribution: Ask leads and customers directly how they found you. “That self-reported attribution will actually be much more impactful in getting a better picture of how people are discovering you than looking at just search traffic today,” Rahul said.

On prompt selection, knowing which prompts to track in the first place, Rahul acknowledged the industry is still in guess-and-check mode. Clickstream data from panel providers is heavily biased. The most reliable approach right now is working backward from sales calls and customer conversations to understand what questions prospects are actually asking before they reach you.

The longer-term metric, once infrastructure catches up, is transactions: demos booked directly from an LLM for B2B, purchases for B2C. “Visibility is a kind of a nice interim metric to have today. Ultimately, it’s about the transaction.”

Don’t Chase Channels, Build a Motion

The conversation ended with a question that’s on a lot of marketers’ minds: with Reddit, Wikipedia, listicles, and YouTube all cycling in and out of favor as citation sources, how should brands think about where to invest?

Rahul’s answer? Don’t optimize for the channel. Instead, build a motion that keeps you present wherever LLMs are pulling from at any given time. “I wouldn’t get overly focused on just listicles, just Reddit, anything like that, because so much is going to change.” What matters is having a consistent off-site strategy that identifies which sources matter for your specific prompts and gets your brand mentioned there, regardless of format.

The brands that will win in AI search aren’t the ones that figured out the right channel this quarter. They’re the ones that built a durable, adaptive system for earning mentions in the places LLMs actually look. “If you have a motion in place that figures out what the LLMs deem most valuable and you get your brand mentioned there—that’s what matters.”

Voices of Search is a daily SEO and content marketing podcast hosted by Jordan Keone and Tyson Stockton. The show delivers actionable strategies and data-driven insights to help marketers navigate the ever-evolving world of search engine optimization and content marketing. New episodes air weekly, covering everything from technical SEO to AI discovery, featuring industry leaders and practitioners sharing real-world frameworks and proven tactics.

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