In this podcast episode, George Vlasyev sits down with Rayze Digital's founder Yaser Ayub, at The GEO Blueprint podcast. Drawing on 20 years in SEO and active testing across LLMs, Yaser covers how AI search actually works, how to measure brand visibility within it, why brand mentions now matter as much as links, and why the fundamentals of good SEO have never been more important. Whether you are just beginning to think about AI search or already investing in it, this conversation helps you figure out where to focus.
Yaser Ayub
Founder of Rayze, helping businesses scale through SEO and GEO for better LLM visibility.
Published:
May 9, 2026
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In this podcast episode, George Vlasyev - host of The GEO Blueprint - sits down with Yaser Ayub, founder of Rayze Digital, an SEO and AI search agency helping brands build lasting visibility the right way.
Doing SEO the Right Way — and Why It Matters More Than Ever
Yaser has spent two decades watching the same pattern repeat: someone takes a shortcut in SEO, it works briefly, and then Google clamps down. He saw it with Penguin and Panda — two algorithm updates that wiped out agencies and clients who had chased quick wins through poor link building and thin content. He sees the same risk playing out now in AI search.
The wrong way, in his view, is optimising for the short term — buying low-quality links, flooding sites with AI-generated content, creating doorway pages, or chasing placement in listicles purely for the visibility boost. These tactics can work for a while. The problem is that they tend to unwind, and when they do, the damage to a business can be significant.
The right way is slower and harder to sell to clients who want results now. Yaser is honest about this tension. He does not make guarantees — not because he lacks confidence in his work, but because external factors like Google algorithm updates and the rollout of AI Overviews can affect performance in ways no agency can fully control. His position is straightforward: SEO is a marathon, not a sprint, and if a business wants quick results, he will tell them this might not be the right approach.
How AI Overviews Are Changing Traffic — and What to Do About It
AI Overviews — the AI-generated summaries that appear at the top of Google search results — are having a measurable impact on organic traffic, particularly for informational content. Yaser's observation is that the effect is more pronounced in B2C than B2B, and that it is concentrated in top-of-funnel, "what is" and "why" type content rather than transactional searches with clear commercial intent.
His response has been to split content strategy into two lanes. The first lane accepts that informational content may increasingly serve AI rather than drive direct clicks, and optimises accordingly. The second doubles down on transactional, bottom-of-funnel content — the kind that drives leads and revenue. Rayze has always been revenue-focused in its reporting, so this shift feels natural: when traffic is down but sales are up, the story is not as bad as the numbers suggest.
He also spends significant time culling content — auditing every page on a client's site to assess whether it is generating any meaningful interaction. Pages with zero clicks and minimal impressions are removed or consolidated. Better crawlability and a leaner site tend to improve overall performance, including in AI-generated answers.
How to Measure AI Search Visibility
One of the most practical parts of this conversation is Yaser's framework for understanding how a brand appears in AI — across all the major LLMs, not just one. The starting point is a straightforward question: what does AI actually know about your brand?
This breaks down into two layers. The first is brand-level: how is the business described, what associations does it have, and how accurate and complete is that picture across different AI platforms? The second is entity-level: for each specific topic, product category, or area of expertise relevant to the brand, how does AI represent that entity, and how does the brand feature within it?
Share of voice within AI is a useful metric, but Yaser is cautious about how it is measured. The risk is selecting only a subset of competitors and measuring share within that group — which produces a misleadingly positive picture. Share of voice needs to reflect the full set of brands actually appearing in AI responses for a given topic, not just the ones you have chosen to track.
Prompt tracking is the third element. Rather than trying to optimise for specific prompts (which is not scalable — prompts vary enormously and cannot be tracked individually), Yaser uses open-ended question formats to monitor how AI responds to category-level queries over time. The goal is to understand evolving patterns, not to chase individual placements.
On the revenue side, Google Analytics is already showing AI-attributed sessions for some clients. One example from the conversation: a client whose organic traffic appeared flat or declining, but whose GA data showed meaningful revenue being generated from AI referrals. Traffic as a vanity metric is increasingly giving way to revenue attribution as the measure that matters.
SEO is a marathon not a sprint.
Why Brand Mentions Now Matter as Much as Links
The relationship between SEO and PR has traditionally centred on one question: did the coverage include a link? Yaser has always valued PR for link building, but the conversation has shifted. Analysis of competitor visibility in AI has shown brands earning strong AI citations from press coverage that contained no links at all. The mention itself — the brand being named and associated with a relevant topic in a credible publication — is influencing how AI represents that brand.
This is a significant opportunity for PR agencies. The implicit message has been that coverage without a link has limited SEO value. That is no longer accurate. A brand being cited, discussed, and associated with a topic across quality third-party sources — YouTube channels, Wikipedia, industry publications, relevant blogs — is building the kind of entity authority that AI systems are drawing on.
Wikipedia is one specific source worth noting. Yaser's team has seen strong influence from Wikipedia on AI outputs, and they have worked on optimising Wikipedia pages as part of their AI visibility strategy. The caveat is the same as with listicles: doing it well works, doing it at scale in a low-quality way is likely to backfire when AI platforms adjust how they weight different sources.
How to Rank on Both Google and AI — Without a Complex Strategy
Yaser ranks on page one of both Google and ChatGPT for "SEO consultant London" for yaser.uk. His answer to how he got there is deliberately undramatic: by doing the basics well, consistently, over time. The site is small — around 20 pages, a handful of blog posts — and tightly focused on a single topic. No attempts to cover everything, no content created for the sake of volume.
Brand building has been the other factor. LinkedIn activity, consistent positioning, and brand citations — the number of people searching directly for the brand name — have all been growing. Yaser sees a clear correlation between brand search volume and strong rankings in both traditional and AI search. Investing in owned and earned channels — LinkedIn, YouTube, third-party coverage — is not just marketing hygiene; it is increasingly a visibility strategy.
On the question of whether businesses need two separate strategies for Google and AI, the practical answer is: not quite. The foundation is the same — strong technical SEO, quality content, topical authority, and genuine brand presence across the web. What changes is the content split (informational vs transactional) and the focus on being present in the right third-party sources. It is one strategy with two layers rather than two separate programmes.
Fresh Content, AI Overviews, and the Citation Question
One of the clearer patterns Yaser has observed is that AI appears to favour recently updated content. He optimised four pieces of content for a client and saw one become among the most cited within roughly a month. A separate example: a newly launched website where optimised content appeared in AI Overviews quickly. The observation is not a controlled experiment, but the pattern is consistent enough to be worth acting on.
The question of what value an AI Overview actually delivers — if it answers the query without generating a click — remains largely unresolved. Yaser's position is that the visibility itself has value, particularly for brand awareness, but that the mechanism for tracking it is not yet mature. What is visible in Search Console is a growing impressions figure alongside flat or declining clicks — the pattern showing up widely in agency reporting right now. Bing has moved furthest in providing citation transparency, showing which pages are being picked up in AI responses. Whether Google follows is an open question, but the incentive is there.
This connects to a longer-standing challenge in marketing attribution. Position-based attribution — sometimes called the 40/20/40 model — assigns 40% of conversion credit to the first touchpoint that introduced someone to a brand, 40% to the final touchpoint that drove the conversion, and splits the remaining 20% across all the interactions in between. It is a framework built for long customer journeys, where awareness and conversion are separated by multiple steps. AI Overviews sit squarely in the awareness end of that model: they may not generate the click, but they are shaping the impression that eventually leads someone to search for a brand directly, or to recognise it when it appears elsewhere. Measuring that contribution is difficult — but dismissing it because it does not show up in click data is the same mistake marketers made when they undervalued brand coverage without direct links.
The brands most likely to win in AI search are the ones with genuinely good products and services, served to the right audience, with a consistent presence across multiple channels.
Ads Are Coming to AI — What That Means
Ads in AI are not a future scenario; they are already being tested. ChatGPT has begun running ads for a small number of selected advertisers, with entry points reportedly starting at $250,000 per agency with significant minimum spend requirements. The CPMs, according to early reports, are high.
The concern Yaser raises is whether ads will feel more intrusive in an AI context than they did in search. People are using AI tools differently — sharing more personal context, treating them more like a personal assistant than a search engine. An ad appearing in response to a query about improving a personal relationship feels qualitatively different from a sponsored result in a list of product options. OpenAI will need to be careful about how this is handled, and there is a first-mover risk: making a bad impression on ads early could benefit platforms that move more cautiously.
The broader dynamic is familiar from the history of digital advertising: the platforms that have the most data and the best monetisation infrastructure tend to win. Google's experience with CPC, CPM, and YouTube ad revenue gives Gemini a structural advantage when ad formats in AI mature. The revenue question is also relevant to the competitive picture — platforms burning cash at scale need a path to sustainability, and ads are the most obvious one.
Which AI Platform Should Businesses Focus On?
The honest answer is that no single platform dominates in the way Google dominates search, and it is not yet clear that one will. Yaser's view is that different LLMs are developing towards different use cases and different audiences, and that for businesses focused on AI visibility, ChatGPT probably warrants the most attention right now — both because of its audience size and because of how widely it is being used as an entry point for AI adoption.
Gemini is the other platform worth watching, and not only because of Google's infrastructure and monetisation experience. Its usage has grown significantly in 2025, and it has moved furthest in providing citation transparency for SEO practitioners through Bing-style tooling. Claude is notable for a different reason — it tends to be used for more complex, task-based work rather than general queries, which may affect the type of brand visibility it offers.
The underlying point is that optimising for all of them from a strong technical and content foundation is more sustainable than betting on one winner. If a winner does emerge, the businesses that built genuine authority and brand presence across the web will be better positioned regardless of which platform it is.
The Fundamentals Still Win — Especially in AI
The closing argument Yaser makes is the one he returns to throughout the conversation: the brands most likely to win in AI search are the ones with genuinely good products and services, served to the right audience, with a consistent presence across multiple channels. Word of mouth is still the most powerful form of marketing. The iPhone spread because it was an exceptional product. No amount of search optimisation substitutes for that.
In practical terms, this translates to a clear set of priorities. Invest in technical SEO foundations — not just site speed, but genuine crawlability, internal linking, and content quality. Understand what AI knows about your brand and where the gaps are compared to competitors. Build topical authority within your specific category. Be present in the right third-party places — not everywhere, but in the channels where your audience actually is. And build your brand; the correlation between brand search volume and AI visibility is not coincidental.
The businesses that treat AI search as a sprint — buying listicle placements at scale, flooding platforms with low-quality content, chasing short-term visibility without building real authority — are likely to find themselves where many did after Penguin and Panda. The ones that treat it as a marathon, and focus on genuinely satisfying their customers, are the ones most likely to still be standing when the landscape settles.
