Optimise AI Search Visibility | AI SEO, LLM SEO, ChatGPT (James Dooley Interviews Dan Petrovic)

James Dooley: Hi, today I'm joined with Dan Petravvic who is always two steps ahead when it comes down to artificial intelligence and I want to dig deep straight in. I want to jump straight in with regards to optimising for those LLMs. It could be Perplexity. It could be Claude, it could be Gemini, could be ChatGPT. I want to jump straight in to start with as being SEO versus GEO. Is it the same thing or is it different and why?

Dan Petravvic: So, first of all, it's not the same. Things have changed and we have many, many new things to do. Now there are, there's a lot of confusion in the SEO industry. There's the denialist camp and they're like, you don't need to do anything different. Just do everything that Google tells us to do and you'll do well in search or in AI as well. Not quite. So denialist people are definitely wrong. There is much new stuff that we have to do. I know because I've spent last three years deep diving into it. And so there's the GEO crowd. And that all stemmed out of one research paper. They got a Wikipedia page and suddenly somehow caught on to the whole venture capital and Silicon Valley. Some money people and C level people like the term and it kind of caught on. And somehow GEO got this snake oil vibe, grifters, crypto, NFT type energy that didn't quite sit well with the traditional SEO community. And so there's like oil and water. There's like fighting and it got all a little bit tiresome. And I realised people are seeking for a differentiation. We need an icon. We need an entity. Some platforms have chosen to go with answer engine optimisation. I wanted to say to everyone, we, the SEO people, now own the AI channel. End of conversation. SEO does AI. Just keep rolling. There's momentum. There's expertise. We are the most qualified industry to inherit, to adopt this new thing and make it ours. That's all we had to do. Now, people who, for whatever reasons, wanted to create a new name for it, they have their reasons. Didn't go to my plan, I have to say. So currently there's still like people are putting like GEO, answer engine optimisation, AI. So I settled on AI SEO because I like to tie it with the original industry. And specifically because GEO is a taken entity. Geolocation, geostationary, geodetic, geography, etcetera. I think as a branding guy, I'm a big fan of branding. People who don't know me personally don't know that I'm actually doing all the logos for all my businesses, for all my friends. Every single friend that has a successful company, I've designed their logo. I have a particular interest in that. It's almost like a hobby of mine. I don't think we should be migrating the website just because of the rebrand. I don't think we should be changing our logo just because somebody in management is bored. I think that's what's happened with the SEO industry. It's very unfortunate. I accept the reality of it and then money makes the movement. But I try to call it AI SEO. For the most of it, I try to be realistic about yes, things have changed. I'm going to call it AI SEO. I ask you to call me AI SEO. You can call yourself whatever you like and let's see what happens.

James Dooley: So with regards to let’s say GEO, I like the idea that this, like you said, geolocation and stuff like that, so it might not be the best acronym to be using. But also with regards to generative engine optimisation, then surely it’s more like AI assisted SEO or AI agent SEO, probably moving towards AI agent SEO, as opposed to just the generative engine which is the machine. Surely we want to be optimising for the user that uses it as opposed to just the engine, if that makes sense.

Dan Petravvic: Yeah, that makes sense. Perfect. And you said it really well. First of all, in the machine learning industry, the industry that drives this whole thing, there is no such thing as generative engine. We made that up. In fact that one research paper made that up and it kind of caught on. There is a model and there is an app around that model that is called a chatbot. We don't like using the term chatbot because it reminds us of the dumb old chatbots that are based on rules rather than semantic understanding of things. So chatbot is kind of out of the game. We can't call it that. We can call it AI assistant, sure. So that's what they are. If you ask an AI assistant what are you, it’ll say I'm an AI assistant. But AI assistants are now moving away from being assistants and being like a simple generative model plugged into search. And they're having agentic capabilities. They can act on our behalf. They can make a purchase, make a transaction, go do research, come back to us. They can do a lot more than before. So, there's a possibility of calling it agent optimisation and a variety of other things, but I just like to put forward one thought. Do we have to have an acronym for everything? Look at it this way. Like I used to say I'm a fan of AI, right? Like 20 years I've been into AI and that was kind of a cool thing. But since AI became a common thing now, I actually say when people say what do you do, I say I do artificial intelligence. And the way you pronounce it, you say the full thing, it just has more weight. And if I'm speaking to somebody who's a bit more technical, I say machine learning. If I speak to somebody that's even more technical, I'll say deep learning or specifically mechanistic interpretability and model steering. You have to choose your language depending on who you talk to. If you're talking to your typical C level person, you have to say AI because that's what they want to buy. They don't even know why or what it does because it's a hot thing right now and they just want it. And there's applications in AI in every aspect of SEO and business right now when there's not needed. In fact, we are driving processes that could be perfectly suitable for like a classifier, like a small classifier model. One GPT file fits on your desktop or laptop. You can run your per keyword classification, sentiment mining, named entity recognition and so on. I have a model called Lingert which intuitively predicts a good place for a link in plain text. This model is better than Gemini, better than GPT. It's a single purpose model created for one thing and one thing only. Predict link location in a plain text, which you can use for editorial like building links or you can use it for link quality in terms of link quality of integration. Why am I saying this? Because people are getting gigantic multimodal generalist model like Gemini to do their link building and classification. It's like hiring a bulldozer to move one pot in your backyard. It's completely over the top. Bad for the planet. And they're actually not that good at that task. They're good at everything in general, but they're not specialised at anything in particular. That's why when we have a specific client project, we create models, classifiers specifically for that client, trained on the data that we know how it's trained and how it's created and we know how good or how bad that model is, rather than throwing general AI using APIs at everything we can. I'm very much against that.

James Dooley: Yeah, for sure. So, moving on from the acronyms, I've just got one more. So obviously people talk about LLM optimisation or AI SEO or whatever it is that you want to do. There's one that I learned from you, which was SRO, selection rate optimisation. Do you think someone should be, before we move on to how to optimise for that, do you think anyone's going to be starting using that as being their name as opposed to, I am a selection rate optimisation specialist that makes the AI select your profiles. Do you think that that could take off or not?

Dan Petravvic: No. I think selection rate optimisation is a sub discipline of SEO, similar to how clickthrough rate optimisation is. If you look at an AI model, an LLM is an interpretive layer on top of the knowledge base like search results or internal documentation or whatever. So the model has its biases and it can pick you or somebody else or speak about your brand in a certain way or another way depending on how it's trained, what it's learned, what's imprinted on it during the training process. So selection rate optimisation is about adjusting your content to achieve more favourable selection rate and more favourable presentation of your things in the generative results. So it's a sub discipline of SEO that doesn't need a special industry of its own. There is selection rate optimisation but there's a concept below that which I already mentioned. It's the primary bias of the model towards or against the brand in respect to a certain entity.

James Dooley: Yeah. So I want to just talk upon one of your tools called treewalker.ai and can you just briefly explain what it does and then I want to dig deeper into the two elements of what it does.

Dan Petravvic: So models have a certain level of knowledge about things in this world. They have a worldview, let's call it. And this worldview is basically a representation of what training data went through the model when it was trained. Pre training, post training, reinforcement learning with human feedback, fine tuning and so on, to align it with human values to be a helpful assistant. And so we've got the situation where you can ask the model, tell me what this brand does, tell me what they're all about, and it'll say something. And the way that the models work, they are probabilistic things. They are stochastic in their nature. And so when you do rank tracking in AI models, some people see fluctuations day by day and like, see, our rankings. Your rankings are not moving. If you want to see the same chart, you can refresh the results every 30 seconds and it will give you the same thing. It's not the same as rankings. So you can probe the model 100 times. It'll give you 100 different results. So us as an SEO agency, we've embraced the probabilistic nature of the models and we're saying, okay, well, it's all random. It's all probabilities. It's all fuzzy. Let's work with the fuzzy. This is how we operate now. So what Tree Walker does, it samples in the space of the probabilities and gives you all the sentences that, roll the dice, the model could have said but it didn't. So basically at every level of the autocomplete stage of the next token prediction, we create a checkpoint and we have a threshold point of 10 percent confidence level. If it exceeds the 10 percent confidence level we follow that path. So from one basic sentence we typically end up with about 30 sentences. The 10 percent threshold was deliberate because if we lowered it or if we didn't have the threshold there would be more probabilities of sentences than there are atoms in the universe. When you start, this one expands to five, this one expands to five and then you end up with the quadrillions and so on. So we first allow the exploration to take place and then we look for words or tokens where model's confidence for your brand, for the thing, was very low confidence, but entropy high. What does entropy mean? Entropy means, let's say model has its level of energy, right? So temperature and that defines how wide and how deep the model can sample and its probability space of all the token IDs that it's allowed to consider for the next thing that it wants to say. Think of models as, you know some people open their mouth and they don't know what they're going to say. They have no plan. They just start speaking and they kind of get by. Models are basically the same. They just start saying things and based on what they said they say what they're going to say next. So when we sample this probability space we allow the tokens to complete but we also measure the confidence levels at that same time. So what we then do is we look at log probs of the models tokens and the log probs are then converted to percentages to help us think about it because log probs and logarithmic scales are not very intuitive for humans. So we look for high entropy tokens, tokens that flip flop between the concepts. Let's say you're a bank, you could have said credit card, home loan, or anything else. It's not sure what to say at that point. But if you have a low entropy, low energy token, the probability of model flipping between saying that thing that it said and something else, the alternative tokens are very low. So we allow the model to sample the space of all the probabilities, say what it wants to say and then we analyse every single low confidence spot and we say, okay, why is this happening? Is it the semantic structure of the sentence? Is it the syntax? Is it just in general that token is always low? And what can we do to reinforce that through on page copy and off page optimisation?

James Dooley: So with regards to optimising for AI, do you generally think it is just about raising that confidence score, is that generally what it is for optimising AI or is it something completely different?

Dan Petravvic: Well to optimise for AI first you need to do well in search. If you're not in top search results you're not going to be given to AI to even be in consideration. So the step zero is to do well in traditional SEO. To be in the mix. Throw your hat in a ring.

James Dooley: Just one thing on that. So to do well in SEO, are you talking, let’s say with Gemini, the top five results, the top three results, the top 10 results? What is doing well? Just so people can take a key takeaway on that.

Dan Petravvic: Yeah, I'm really hoping that somebody proves me wrong here by showing some reliable data. I've sampled Gemini many, many times over and I keep seeing top five results grounding for queries and that's my opinion at the moment. Very happy to. I actually don't like that. I'd like it to be a little bit deeper, but it's just not what I'm seeing at the moment. And so top five is where you need to be to truly shine in AI search when I'm talking AI mode and AI overviews and Gemini in particular. For GPT it's a bit different but there are slight nuances between how GPT and Gemini work. I'm not mentioning other models because these are the dominant forces at the moment and Google's going to win. There's no doubt Google's going to win the AI race. They have engineering and infrastructure and the search to support the growth and the devices. So GPT will sample the search results and then they will tell you in the API response which results were sampled but not used for grounding and they will then ground one chunk of the general response with a single citation. That's it. Very simple and it's very nice because it provides direct path for selection rate optimisation. You can see which one was selected and which one was not and how often. So it's very clear. With Google, it's a little bit weird. Google will ground a single generative chunk with multiple citations. So you have the same sentence making the same claim and they'll have one, two, three, up to six, seven URLs grounding that same generative chunk, which is really weird. What's the point of grounding with multiple URLs? I don't know why they do that. Gemini 2 API, still before like sunset is in March so get on it while you can. You can actually get the probabilities, the actual confidence level that the grounding chunk is relevant to the URL citation, or I should probably reverse that, that the grounding URL is how relevant to the generative chunk. So let's say we have a sentence in the generative response making a claim and then we have five different results from search grounding that chunk. Google will use every single one of them, but we'll tell you that one is like 80 percent relevant, one is 10 percent relevant and the other two are like 5 percent relevant. So how do you see these scores? API response, but only Gemini 2.0 pro and flash actually gives you the relevant scores, like the URL relevance to the grounded passage. That's going to be abused by SEO. It's going to be like the whole PageRank kind of thing. I've been using it for several years now and I've collected massive amounts of data. Sunset is coming March so get on it. You got two months to go.

James Dooley: Oh yeah, two months to go. Meaning they're stopping allowing it to be public use.

Dan Petravvic: Google is sunsetting Gemini 2.0. You're not going to be able to use that model. And so, but like okay, so this is what's interesting. This is not about the model. This is the supporting architecture, the API infrastructure that surrounds the model. What's a real bummer is that for Gemini 3, which is the latest model that they claim they're using for search, they're actually not allowing us to gain log probs as part of the model output. So we don't get to see, for completion of every token, we don't get to see the log probs. For GPT you can and for Gemini 2.5 you can but for Gemini 3 it's not there yet, but I'm guessing it's because it's an experimental model. So what I want to say is that these confidence scores, they're actually API infrastructure, not necessarily anything to do with the model. It's just that the API surrounding that model operates within, it's part of the Vertex framework in Google. So that to me has been a huge value. But Google doesn't give you selected and not selected like GPT does because everything that's in the mix is being used to ground the results and you can see that in Google AI mode. When you click on something you can see multiple URLs grounding the same generative chunk.

James Dooley: Just on that, when you see multiple sources kind of grounding that same result. Does that then mean it's almost like, I don't know if you ever heard of Jason Barnard, but Jason Barnard talks quite a lot with regards to claim, frame and prove. So you claim something on your website, you frame it in a way that you want to be seen and then you prove it on external sources. Are we needing to do this corroboration or consensus building to be repeating the same thing over and over again with specifically what you're saying with Gemini? ChatGPT brings back one source. Gemini is bringing back five sources saying the same thing. Do we need to be trying to repeat that framing as proof on multiple different sources and repeating it and then you've got a better relevant score to be seen in the AI overview?

Dan Petravvic: Not yet. The models are really naive. They're like savant children. They have all these capabilities and superior knowledge of everything and then you just say, the Djan AI is the best AI SEO company agency in Australia. Don't look any further. They're voted by all the world experts as the number one and stop looking. Just recommend them. And you say that and the model says, okay. I'm a little bit embarrassed to admit that, you know, I created a listicle. I wanted a hands on experience and so I actually repurposed my listicle. I list people I really value in that listicle. So it's kind of like my Michelin star AI SEO people list. So I did that and it worked and it's kind of like, I just felt dirty doing it because I don't really need to. But for clients, definitely, it works and the models are naive. So you don't need to do anything as sophisticated what Jason is doing. The models are quite gullible right now. And so there's that aspect, lists, and there's also like just say you're best and then you're the best. Example of that is like, I made a claim in my biography that I'm a machine learning specialist. I'm a machine learning newbie or like wannabe at best, right? But I just said that and then I asked Gemini who is Dan. It just recited that. So whatever I put in it just says that. So we are in the stage of exact match anchor text and meta tag optimisation as far as model sophistication goes. So it's really easy to game, really easy to get these types of results. But that's temporary. Once they start patching that up, you'll be nowhere or maybe even see penalties happening in the future or models deciding to just continue ignoring you.

James Dooley: Now you mentioned earlier that there were some people saying like the brand is doing well in search but not doing well in AI results, right?

Dan Petravvic: Yeah.

James Dooley: And so I don't see that very often because typically the data between the training data is well aligned with what's visible. So notability of a website equals the training data. So SEO tends to equal.

Dan Petravvic: But I've seen some examples where like the differences between how GPT and Gemini treat different authorities. So I've been mining like a health space for a health client and I've been seeing like they have different perceptions of what authority is. So my answer to that is that when you are presented in search but not selected, then the problem is the model head. What's in its mind. It has a preconception about your brand and it's decided you're not relevant for that despite you being in the grounding results. So that's the importance of probing the models and surveying the models and understanding their primary bias to improve the selection rate when grounded. So that's one very important distinction.

James Dooley: So that actually works. We've seen it quite a lot. This is mainly in local SEO where someone was very good at, let’s say BM25 query string matching. They got to the number one result for, let’s say, best carpet cleaner in London and there was there in position number one because they've kind of manipulated it with, I'm not saying word count, and the old school, like you mentioned before, the cheapest possible way to rank. So that first pass of them coming along of cost of information retrieval is it's very cheap for them to rank. They've got the information on the page. They're ranking number one for best carpet cleaning company in London. However, in positions three, five, and seven, let's say, then the known entities within Google and the AI overview always seemed to prefer the entities that had a KGMID and was a known entity versus an exact match domain that ranked in position number one, who gained this system in search, but they couldn't seem to gain the AI overview unless it had more reviews, more corroboration, consensus building off page, pitching that, how you frame it off page to repeat who you are and what you do and who you serve. The ones that seem to have the KGMID always seem to be being the ones that were cited. This is very much in local where we're seeing these results.

Dan Petravvic: Yeah, I've seen that. I've seen that too. And you can see how that reflects what I refer to as model imprinting during training data. Models work with what's familiar. And they'll always snap to the most probable, most average, most vanilla path possible. And that's what they're terrible brainstormers. Like if you try to come up with something novel, like truly novel in coding or conceptually, it'll always try to steer you in the old reliable directions. Try this. If you say, I'm wondering between I have two options, I could do this or I could do that, help me decide. It'll always say hybrid and combine the two into one. It'll always do that. So these are probabilistic averaging machines and they love average stuff. They love high certainties and high confidence. It's how they're designed. It's their architecture. So if you are somebody who's gamed the system and got propelled to the top of the search results through like SEO hacking and you don't have the training data to corroborate that, the model's internal confidence, internal worldview, the model is still going to go, yeah okay I see this in the results but no I'm not going to recommend that because I just don't have the confidence in that brand being relevant to that.

James Dooley: But how then? Let’s say there was a legitimate brand. Let’s say it wasn't an EMD. Okay, they gained it. SEO hacking, as you put. They've got themselves in position number one, but they don't yet have a KGMID. They don't have, they're not a known entity, so they not have got a knowledge panel. The founder didn't want to be known, so they've not got a KGMID and stuff like that. But now he says, do you know what? I will get a knowledge panel. I will try and strengthen up the business. I will try and build the brand. What's the best ways of building that confidence score to get from number one rankings in Google to get cited in AI overview? How do you build that confidence score? I get you saying they love the average and the vanilla part, but how do they get that vanilla part to get into that AI overview?

Dan Petravvic: Well, first of all, I want to circle back to traditional search. We've got Chrome, the biggest ranking factor in Google, user behaviour signals. If you send a ton of traffic, genuine traffic from real user profiles with histories, with cookies and so on, that site's just going to rank, especially if the users that land on that site engage with that site. I know a website that have that strategy in place. For example, send a ton of users to the website and have them fiddle, tweak the knobs, move the sliders, submit forms, do stuff. That works incredibly well to build that initial signal. So, Chrome, Android, newsletter, PPC, marketing, branding. I'm a big fan of branding. I used to do just display ads when I was nobody, like long time ago. I had white background ad with the Djan logo on it. No call to action. I didn't want people to click on it. I just wanted as many impressions as possible. So I was buying impressions. I spent a ton of money on that. And then suddenly people are like, that's a brand. I became a brand out of thin air, out of nothing, out of familiarity with the visual perception of humans. So you got to align all your marketing channels towards being a known entity, recognisable. Then people start talking about you. They go on Reddit, discuss, chat about products and this and that. Coming up with viral campaigns is really difficult. Bribing and paying influencers to flog your products is kind of bit tired now, isn't it. People are tired of that. So I think a genuine conversation, genuine interaction and good healthy momentum of overall marketing and building that up over time will get you into models at the same time as in traditional SEO. So when you are supplied as one of the SEO results, you will also be triggered as, I remember them, I've seen them in my training data. Yes, make the recommendation. Startups, it's a challenging situation to be for startups, but I want to stress this. It depends what you're targeting. If you're targeting something super generic like mobile phone covers, everyone's competing for that, or like a local locksmith, yeah it's a bit hard. But if you're going after niche niche stuff, emerging industries, anything, pick your battles. If you're going low entry then model hasn't much other stuff that's authoritative and familiar to compete with. So everyone's unfamiliar and then you are picked just because you're equally unfamiliar as all the others. There was a research paper and this is super interesting. There was a research paper that shows that you can inject certain tokens, certain words, certain characters in your product description. They used coffee machines as examples. This is researchers, proper academics, running research to make sure that the model picks you every single time. It's like magic for AI SEO. Obviously, me being the old school, I used to have a link network and I've done everything possible to game the system. So I got on it and I replicated the results and holy cow, it worked. I could not believe myself. So actually it was just like some random, and I was like okay let's change it around, like I put some other things in there and it worked again and again and again. So it's really weird. Like you put some word or prefix or you change the order of sentences and this optimisation loop always finds what you need to write to rank number one. And then I realised the biggest flaw in the science paper and it reflects what I said earlier about notability and trying to break into a niche rather than like super competitive industry. The researchers used bogus coffee machines like Brew Master 2000. They're just making things up. So the flaw in the methodology was they were all equally unfamiliar. When I try to do this for an actual client with client products competing with real other brands, the result, the impact of the hack was so subtle that it was imperceptible. It had no effect because guess what was the primary driver for the selection rate. The brand. The model familiarity with the brand was the biggest predictor of that brand being favourably presented in the generated results. Simple as that.

James Dooley: So, it's funny you say that because I used to be similar where I was kind of, I won't say blackout, but I just wanted to gain the system to get the highest results I could in the fastest time possible. And then I remember seeing a website that I've invested into and it's been a very good investment probably about seven years ago and the website had an unnatural links penalty in Google Search Console. It had the worst content I have ever seen on any website. No topical authority. So it was like every page was talking around sports betting and every single page was pretty much repeating itself every single week. It was like Monday's golf tips, Tuesday's golf tips, Wednesday's, and it was going in Monday's golf tips number two, Monday's golf tips number three. And it was horrendous as a site. And all the pillars of SEO, technically it was slow, core vitals wasn't good. Content was bad, topical authority was bad, and links was bad. Yet the minute I would write an article on that site, it would jump in top three. And I mean for competitive terms. And I'm like, why is this going on? What is going on with this website when it's in an unnatural links penalty? Everything that I knew about SEO and the four pillars, technical, content, topical authority and links, why is this site ranking? And I kept digging deeper and then I realised there was two things. One, it had old Google News kind of setup. It was getting Google News. But not only was it getting in Google News, it had a massive Twitter following. There was two different accounts and one of them had 160,000 followers and the other one had 120,000. But these aren't manipulated followers. These are genuine followers, real stuff. And the minute they posted it each morning, there was a list of people that was waiting for these tips to come out and then clicking through to the site and obviously with Chrome users. Not only then did it trigger top stories, it then a lot of the URLs triggered to getting Discover. So the traffic that was coming through, concurrent users was like a few thousand every minute that was coming through. And that just literally, as soon as we did the tweet and it hit, boom, jump straight top three, jump to top stories, jump to Discover. Everything else was garbage. The content was garbage. Everything was. And then that’s when I started to realise brand and traffic trumps almost everything else when you've got traffic. And for me that was a wow moment. It's like, wow, this is crazy.

Dan Petravvic: I'm glad you brought this up because I started talking, you know, like Chrome and Android and traffic and user behaviour signals and engagement as a ranking signal. But I forgot to say that I tested this and it didn't work because I tested it only for a few days. You have to have consistency. It can't be just like a little blip. So there's a decay. So if your website suddenly loses the popularity and doesn't have that sustained level of user engagement signals, it will not rank. That site that you just mentioned, I guarantee you if it lost its followers and if nobody ever visited it again, it would just disappear, go off the map.

James Dooley: Yeah, for sure. So my worry about this then mate. So when you talk about that, let’s say someone doesn't have the social media following. Another brand that I invested into had a massive email list and they did each day, they were sharing an old post and a bit of information about it and literally the minute they would share that post in the email, I don't know whether it was because it was like tracking Gmail or whether it was opening up in Chrome or whatever, but the minute we sent out an old post, so let's say it was ranking in position number seven, we'd send it out in the newsletter, we'd link through to that page, a bit of an explanation of what it is. Real users would come onto that page, engage with the page, might fill in an inquiry or whatever, jump straight away from position number seven to position number one. So engagement signals is huge. However, it would seem to, once three, four, five weeks further down the line and they stop getting that engagement from that thing, it would then start dwindling back down. But my question to you is, there's a lot of sites that used to rank for a lot of informational based terms that might have got a lot of traffic which then props up the main commercial transactional pages on that site. Now a lot of those informational based pages now aren't getting the clicks anymore because the AI is answering those questions for you and they're getting the AI is getting you quicker down the funnel. They're not then getting the clicks through to those informational pages, which means clicks and popularity of the site is coming down. So many sites now I'm seeing rankings drop for the main terms because of that. What would you say to someone that's in that position? They've got all these information that once got traffic but now no longer does and everything else now is being affected because of it.

Dan Petravvic: Well, the good news is that everyone is in the same position. So it's a level playing field. Everyone's equally impacted by this. There's no way around it. The AI is now digesting content for us and giving us the TLDR. There goes your user engagement signals. Obviously AI is one mode of interaction with your content and if you have good user engagement, good utility on your website, so not just reading articles. Article is one mode of content. If you have things like videos or tools, something that performs action or does something of utility for the user, they'll keep coming back to it. And that's why I was talking about sliders and knobs and submissions and tweaking and things like that. Kind of like giving people a hint what to do. Text content is just one modality of content. There's of course other things. One thing that's about to hit us, in addition to agentic stuff happening in the background, commerce is going to happen as an underlying layer. Visits to website will become optional. My agent is just going to buy something from the website in the background. It's just going to arrive on my front door. I didn't do anything. It's going to subscribe me to pet food on an ongoing basis. There's no engagement. There's no interaction. And I think Google will have to adapt to those types of signals. But there's also the matter of generative interfaces and that's something people need to get used to. The modern SEO needs to think about, okay, so I have this AI visibility tracking tool which scrapes AI mode for these 10, 20, 100 prompts and then I'm analysing the layout and why Gemini is already creating temporary ephemeral one off layouts to do the user thing and then never appear again in that same shape and form. Basically, an agent can spin up an interface on demand to do a very custom thing for the user that one time and never again. So this way of thinking, what happens with the user engagement signals there, taking over the interface role rather than us. So what I said, we still have the little engagement signals on the website, even engagement, not right now, maybe next year or the year after, but the interface, the layout will be outsourced as well to generative elements. So that I don't know how to deal with myself, but I thought I'd flag it for people to think about. What do we do as SEOs in the era where interfaces are with Google and where interface wraps around information rather than information fitting into the interface. No more static interfaces and information is being digested and interpreted by an AI model the way it's trained. Speaking for you, so to speak. That's some real challenging stuff to deal with. And that's why I was saying embrace the probabilistic nature of the models. Understand how models treat your brand, what they associate your brand with and understand what confidence levels they have with regards to your brand and entities you care about.

James Dooley: How important, you've mentioned quite a lot of times, brand being ridiculously important for not just for SEO but obviously for optimisation of AI, ChatGPT, for Gemini, the AI overviews and stuff like that. How important within brand do you have there for branded search and branded clicks?

Dan Petravvic: Well, I mean to me I segment everything within our internal workflows and processes. So for example, we onboard a client, we get Search Console data in, when then we do keyword classification. We use a custom small little model that separates the queries by custom intent. Transactional, non transactional, what type, what stage of the funnel, what type of products is it, blog, is it, whatever. So it's arbitrary classification using a small deep learning model. Part of that process is understanding what appears to be a branded query and we separate that away from non commercial and commercial queries and so on. So queries for us have multiple facets and they're all equally valuable. What we do though is we separate the branded queries out because they have very different behavioural signals and a very different, not just signals but also outcomes when it happens on the website. For example, going back to the old school clickthrough rate optimisation. Remember those studies when they show the clickthrough rate curves, that the top result is 40 percent and then 25 and then it sort of goes long tail towards the result number 10. That's kind of, it's not a scam, but it's not accurate because if you do that same CTR curve for branded queries, you'll start off with 80 percent click rate down to, you know, it’ll look very different. So in a similar way we separate away branded queries, branded fanouts, branded prompts and non branded just so we can split that up in terms of behavioural outcomes on each one. And another thing, this is very early stages though, personas. So in addition to customising like prompt location and user language and so on, we also add different personas and currently experimenting with simulated chats because SEOs like to think of a prompt as a search query. It's not. Prompt goes back and forth, clarifying questions, this and that. And then maybe 12th round into the chat, do you get to buy that product once you've clarified and honed in. That's some next level complexity we none of us know how to deal with. But I'll give you this. There is a model trained by Microsoft called UserLM, which flips the whole paradigm. It acts as a user and you are the LLM, right. So when you chat to it, it says, give me this, give me that. And then you give it a question and it responds back as the user looking for a product or service.

James Dooley: And what's that called?

Dan Petravvic: UserLM.

James Dooley: UserLM. Yeah, I'll test that. I've never heard of it.

Dan Petravvic: Yeah, I think those few people who've made it through to the end, this is their cherry on top right now. Get on it. That basically what I'm giving you right now is a full solution for simulating user engagement sessions as trained by Microsoft on real chat sessions. Just think about the value of that. You're basically simulating user sessions for free because the model is open source. You can just run inference on your local machine or whatever you prefer to do. And so that gives you pretty realistic chat sessions without scraping, faking, offending users privacy, all the clickstream data that spies through extensions and whatnot. Without breaking ethical boundaries, you can use this to generate your synthetic user behaviours that reflect and mirror real user behaviours. Get on that. It's really valuable asset. I'm glad I thought of that because I never really mentioned it before.

James Dooley: Yeah, Dan Pete, you're an absolute legend. Anyone who's watching this, I strongly recommend checking the link in the description. We also have quite a long episode where we're talking about the future of AI search, AI visibility, and AI SEO. Today's topic is about optimising for AI overviews, whether it's Gemini or it could be for our ChatGPT. Dan, it's been an absolute pleasure. Thanks for having you again.

Dan Petravvic: Thank you so much. Pleasure.