How to get discovered in AI search

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0:41Welcome to another episode of the Practical AI Podcast. This is Daniel Leitnack. I am CEO at Prediction Guard, and I'm joined as always by my co-host, Chris Benson, who is a principal AI and autonomy research engineer. How are you doing, Chris? Hey, doing great today, Daniel. How's it going? It's going great. It's good to see you. You're visible to me, and that's an interesting part of the topic today. An awkward segue into how do things become visible to us on the internet these days, which seems to be increasingly through AI platforms, AI chat interfaces, answer engines, whatever you call them.

1:27And today we're privileged to have with us Liam Dunn and Ben Moore, who are co-founders at Discover Labs, to talk through some of these things. Welcome, Liam and Ben. Great to have you. Good to be here. Thank you. Yeah. Well, for those that maybe are less familiar with this topic in general around AEO, GEO, answer engine optimization, AI visibility, whatever kind of term is around this, and maybe there are differences between those terms. But for those that aren't as familiar, could you all give us a context for kind of what those things mean? And then also like how you're involved in those topics day to day, what you're kind of doing at Discover Labs, which is kind of the context that you're doing some of the work that we'll talk about.

2:17Cool. Yeah. So I would say, and everyone's got a different opinion on this, so feel free to take mine with a pinch of salt. So I would say AI search is like the broad category. And then within that, you'd have answer engine optimization. Some people say, which is AEO, some people say GEO, generative engine optimization. I view those as the same thing. And I just call it AEO. Honestly, for a very simple reason, there are a lot of venture funded companies that have spent a lot of money on that term. and so I'm just gonna fly behind them and lean into it. The background of us, so how we're involved with this, so we're co-founders at Discovered Labs.

3:05It's an organic search agency. So we provide end-to-end services. Now, organic search for us splits into two buckets. You've got like traditional SEO and you have AI search. So traditional SEO, we want our clients to be at the top of Google whenever people are searching related keywords. AI search, we want our clients to be appearing inside LLMs in a way that they want to be. And so we view that as like the AI search side of things. Gotcha. And I guess maybe for context, people might be familiar with like SEO, search engine optimization. And is it maybe help orient us is like SEO, is that still a thing?

3:45is this topic basically replacing that? Is it kind of, uh, how do the, how do the two interact? I mean, this is always, I guess this is always a fluid thing, but if I am a company, um, this, you know, I'm, I'm a company in 2026, like what, uh, what, what is most important and what, how are people thinking about the effort they're, they're putting into maybe traditional SEO versus these topics? Do you have any thoughts there? I do. Feel free to, but I can waffle too much. So feel free to ask me questions. I'll go deeper. But ultimately, you won't hear me say that SEO is dead. It's not an opinion I hold.

4:30I just think the space has grown. We're doing the same three jobs that's on page. So looking after your owned website. We're doing off page, which is building brand authority. And we're doing technical, which is making sure agents and Google systems can access your website and understand information. It's the same three jobs. For me, it comes down to a matter of tactical priorities. And I think this is where people will like get lost. I do think there are some things, like depending on what the goal is, right? Like to rank number one on Google for a commercial keyword, you're probably going to use different tactics than if you were to optimize for a surface area like ChatGPT.

5:16Again, you're going to be doing the same jobs. You're going to be creating content. You're going to be building the brand of your company. You're going to be optimizing for technical SEO. But the tactical priorities is where it changes. Like, for example, what does good content actually mean from a perspective of Google versus ChatGPT? so ultimately seo is still a thing it's now expanded we now have aeo those together i call organic search um we're doing the same three jobs but the tactical priorities have just just changed a bit i would say could you talk a little bit about like if you are uh you know a business owner out there and you're you're you're marketing you have your website out there you got your social.

5:58How has this changed? Like if you were to take a snapshot of the industry and a long time ago, I was, I was in the digital marketing industry for a while. And, and, you know, that was obviously before AI came thing and you were focused on these things minus the, the AI engine optimization. And so how has that changed? Like how, how is the thinking of, of the marketing department changing to accommodate the fact that you now have this whole way that people are going out and searching. And, you know, all of us are AI first. That's very different from maybe 15 years ago. How should business owners listening be thinking about that in terms of how they approach the whole thing?

6:40Yeah. So broad topic. So it is. Sorry. I'd say, no, no, no, it's all good. So I would say the first thing is buyer behavior, right? So people researching and consuming information inside LLMs has changed buyer behavior, right? And so the symptom of this is people will see that, hey, like, where's all my organic traffic gone? And it's because that organic traffic, a big chunk of it was created by people consuming information on your website. But if people are consuming information inside an LLM, the LLM is the new website visitor. They're taking that information, they're chewing it up and they're spitting it back out to the user inside an LM.

7:22And so you've now lost those clicks. So I think that's the first thing. And so the implications there are, well, how do we measure that? And that's really challenging. Like I'm a marketer, Ben is the engineer. And measurement has always been like a nightmare for marketers. And so this concept of zero click researchers has been around for a while. I think AI has just sped that up a bit. You know, we've been dealing with that on social platforms. I think now organic search is starting to feel that. So definitely a change in buyer behavior. You know, it's probably very similar as like when the mobile came around.

8:02I think we're seeing some changes there. And then also, I kind of touched on it there. There is now just a new visitor to your website. So websites for the last however many decades they've been around have really been optimized around the human visitor. That's why we have like user experience. How do we make the navigate? That's why websites look the way they do today, right? Like how do we shape the navigation bar, like conversion rate optimization, all of these things are for human visitor. Whereas now it's these agents visiting your website. So I think there's some interesting considerations there of, well, what are they looking for and what state does your website need to be in for them to be able to access information and understand you?

8:52And so I think there's some considerations there. I don't know where this ends. I know there are a lot of companies out there that are building towards this. You have like one version of your website for humans, one website version for agents. um but i'd say those are like the and then then obviously there's a bunch of implications like downstream of all of those things um so does that answer your question i have to go too deep deeper into any no it's a great start i appreciate kind of laid the landscape out there uh and i know daniel has one but after that i got plenty more yeah yeah i think my what triggered in my mind liam as you were talking about that is is that measurement piece one of the things that i think I appreciate about your all's approach and both from a company standpoint, but also a research standpoint is you're interested in knowing actually how the mechanics work around these things and understanding it at a deeper level.

9:48Ben, I'm wondering if kind of turning to that technical side, you've published a variety of research over time and you'll have more coming. I heard a little bit about that before the interview when we were talking. But for example, like citations or mentions in these answer engines or in AI systems, from a technical standpoint, like obviously I as a human can go into ChatGPT or Gemini or Cloud or whatever, and I can test out some prompts and see what's cited. How from a technical standpoint, like what's required from the technical standpoint to actually have a scaffolding and a mechanism that you can track your, like, what does it mean to track your visibility in these systems over time?

10:37What are the relevant things? Like I mentioned a couple of things, citations mentions, like, what are the relevant things that you're looking at? And at a high level, like, what does it take to put the right tooling in place to actually measure and track that? Yeah. As Liam said, it's a big question, but it's a great one. So I would say this is probably the thing that has probably shaken a lot of the SEO industry at its core a lot, in the sense that everyone kind of showed up in the past with traditional SEO and said, look, great. Let's kind of color by, paint by numbers kind of thing. You fill in the colors and we do this, we do that.

11:14Everything's great. Everyone's happy. And no one really actually thinks about what's going on here. And you could go into like, essentially, unless there was a Google core update, essentially, like the rankings were essentially very deterministic and got a set of keywords. And it was like pretty finite, pretty manageable, all pretty controllable. And again, paid by numbers. and now we've kind of like gone into a system and go really deep on this as needed but um we've gone to a space where uh there are sources of like a non-determinism or stochastic elements that people need to start modeling around and the seo industry and we've seen it as a whole uh doesn't have like great statisticians or like great um data science literacy just being blunt um and if you're any serious like statistician or control engineer you would look at the system you're probing and you would say well like what are the requirements on the signals i'm trying to measure here in order to bound the noise on these these these these uh these measurements essentially and this and the the quantities you're trying to measure uh and then you would say okay well then you'd set up your model of the system you like essentially a lot of these lrm agents are essentially gray boxes they're not totally black boxes there are research papers out there there are hints in the the in the network traffic and some of the if you can decrypt the packets there's also information there uh and there's also like distilled models so when i look to the training data you can also figure out like some information from the distilled models and so the thing is to set up basically uh basically an experimentation a kind of engine or pipeline whatever you want to call it and then say well how do i bound the you mentioned their citation rate mention rate share a voice how do i bound the uncertainty on those metrics and what do i need to bound to basically bring that uncertainty under, let's say, 5 % margin of error.

13:02And to be honest, no one in the industry is really doing this or even really thinking about it. People go on, they buy Profound or some other thing, and they're like, here's my prompts. Let's go away, go do this. And Profound aren't interested in it either. They just like to sell you something. And then everyone says, oh, look, number goes up. How lovely. And then the SEO industry has an incredible ability to point at numbers going up and claim that it was them. and then has an also incredible ability to point at a number that's going down and claim its Google core update.

13:38So yeah, I just think in general, I can go into how we think about this and how the various systems are displaced here and how we think about bounding this randomness. But in general, you need to approach it like how am I going to model this system that I know hints about it with the data we have and then how do we bound that um that basically uncertainty on those quantities we're trying to measure for as you mentioned there chris like for the business that has whatever they're selling um you know gardening tools whatever it is right that's that's uh that's what people need to be getting to and no one's really talking about that um and i said that it does take a lot of time uh but i also think we've got to the point where it's like we've got to like stock market dynamics like there was a change in reddit recently and you know no one also really knows is it meaningful So lots of times we have to come to our clients and say, so let's say, I'll give you a perfect example, right?

14:29Yeah, there'll be like, you'll probably have CNN on the background there. And someone will say like, or ABC News, whatever it is. And then they come down and they say, oh, Dow Jones is down 5%. Everyone freaks out. If you actually look to the standard deviation over the last year, you'd be like, well, this is not statistically significant. It's a very basic check. Like it's very basic, but we need to print news. So we do that. And the SEO community has a bit around that. And also people just want to like get eyeballs on stuff. But again, a lot of people waste a lot of marketing hours and a lot of engineering time around things that aren't statistically significant.

14:59That's a very basic thing that we can check. That's not like, that's not rock science. But there's a very simple example. Anyway, that's why I think we're like, I don't know, like state of traditional SEO, where we are now and where people are freaking out around it and where we need to add.

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16:50So Ben, I want to ask a follow-up question. You did a great job at kind of laying a foundation for maybe some of the things that we should be, some of the things that we should be thinking about statistically as we approach this topic. I'm wondering if you could help our listeners understand, like if I put in a prompt to one of these systems and a brand shows up or a thing that I want to show up shows up, what are the mechanisms by which that can show up? And I know you've done some research across citations and web search versus Reddit versus training data. Before we get into any of those very specifically, could you help us understand what happens behind the hood?

17:38How might I show up mechanically in one of these? systems yeah uh so let me dig into it just before i do i would say to anyone that anyone listening for sure do not run down a rabbit hole of being like this is this one prompt and i would love to rank on it uh you want to basically model like if i'm like basically a topical uh domain standpoint and get into more of that but basically what happens is uh essentially your prompt is fed in it's uh it's tokenized and then ingested by the lm um and then there's a essentially a a reasoning stage, right? So basically, the transformer basically has embedded your prompt and now looks at all those tokens and starts generating its reasoning stage based on its training weights.

18:22And basically, it's running this forward pass. And there's typically two sources of randomness. And the reason I'm mentioning this is kind of important, it's like the kernel function. So it's like floating point error in GPUs. That's one source of randomness. And the second source of randomness is the actual temperature itself, like deliberate temperature randomness. um that generates the next token and then the final part of this is like it's also i was speaking with me that this is now there's now uh fingerprinting on these lms so they'll also nudge and create hashing functions in the background to know where the comic came from anyway so it goes through this reasoning phase let's say you say like a best um i know gardening service in let's say dublin and arland i'll use that example and it'll then start reasoning and saying oh it'll have a certain set of criteria it'll like do some reasoning before it makes any queries with its retrieval engine, and it's exposed to a series of tools, and one of them is also going to be the retrieval engine.

19:14I'm saying a series of tools because when we get to agents, they could be exposed to a whole host of other things that you're not aware of. Then it accesses this retrieval engine, creates a set of query fanouts, you've probably heard the term, and then pulls back in basically a set of initial candidates essentially to be pulled into the context window but before that happens essentially they look basically it's not always exactly the same algorithm but deep mind had an agree style algorithm it's called and basically it's a way to avoid hallucinations and so once they pulled in these let's say about let's say 40 to 60 sources from various places it's done this um let's say another set of um analysis on it which is is almost across the board it's very similar to the agree for algorithm from deep mind it's very similar in anthropics models it's obviously it's a gem analysis models and also chat vt we've seen this in our own analysis uh and also a claude moved to it recently when they moved to fable and fable 5.1 essentially what it's doing now trying to see like well what is the consensus so i can basically use like basically majority voting systems so i don't make a mistake i don't have that estimation uh and then it says okay cool great this is our kind of like distilled set of context we want to work with now like let's say generate the final response essentially from that cleaned up uh context uh and this actually kind of ties back to like reddit people freaking out about the final citation again the way i look at like ai visibility everyone's like oh we'd love to know about citations dimensions in the final like output but there's all this other part before we got there so i talked about you know the weights how you're showing up in the weights and we did some research on this recently how is the model reasoning like what is what is it thinking like when it when it makes like core decisions around your domain or be that bottom of funnel top of funnel whatever it is it has a uh criteria it's going through its internal logic just like a human would essentially it's not a human but you get my point and then finally you have like your retrieval engine and a response but you really need to be looking across all of those four pillars in order to dominate your domain that's how that's how we look at it for all of our clients there there are lots of things to dive into but before we do that i'm curious I actually want to go back to Liam for a second.

21:29And as I'm listening to Ben explain these things, the thing in my head was kind of something that you were saying early on about how it changes behavior. And I'm curious as a marketer, I like the ability to go back and forth between the technical and the human behavior side. How is that changing how the human, now that you have the agent going and doing this, what does that mean for the human that now has this proxy in the agent going out and doing that? And how is that changing the behavior and potentially the transactions that are following up on that? Just to kind of come full circle for a moment.

22:09Yeah. So I guess I'll look at this from like demand and supply side. So from a demand side, the buyer, I think this is just a much better deal, right? Because if we look at search behavior pre-LLM, trying to find relevant information on Google is just a nightmare, right? Because, you know, us marketers just ruin everything. And you'd look for things and like, you just get the list of 10 URLs that you then have to go visit and research. And whereas if we compare that today to like chat-based LLMs, it's more conversational. So I could be like, hey, I'm Liam. this is my company it's in this industry we're doing this revenue we're really struggling these are our core constraints at the moment we're looking for this solution these are the attributes of the solution you know we're looking for a piece of software must have a free trial must charge monthly must integrate with HubSpot or CRM all of these requirements I can front load that and then the LLM does its thing and it comes back with personalized recommendations now I'm not saying that marketers are you know that's our job to influence those recommendations but it's just a far better deal than like clicking through all those websites and finding the information myself and like me personally i just use it for everything right date night you know meal prep like buying software everything so from a demand side i think it's it's a greater deal and so therefore i think more people are going to take the deal and from a supply side the people who are the vendors i i would say i probably look at this uh from two ways so number one if people are front loading all of that context that now creates a really long tail distribution of queries that those models are using it's no longer just best cold email software which is what people would search on google it's all of these entities and things are kind of hang you know um appended onto the end right the integrations the the business model all of these unique requirements so that creates this long tail distribution of queries now the great thing about long tails is they're less competitive and so I think this has really been where the opportunity has been over the last year is where ultimately I would simplify it down to relevancy be authority now there's some nuance in but this is really where the edge was and this is where say if I'm competing against HubSpot who has dominated Google SERPs for all the money keywords I'd love to rank for well now I can create relevant content for those super niche queries that my buyer is searching inside an LLM so I think there's that perspective is the long tail distribution means we can create relevant content that targets there.

24:59And then I think there's the authority consideration is, okay, well, if everyone has relevant content, how does the model then reason over who to trust? Who does it decide who to trust? Historically, with traditional SEO, we look at page rank. It's how we know Google assesses authority of pages and domains. Again, it comes down to consensus. it's not as weighted it is my understanding you know google have publicly said this it's not as weighted nowadays um what's like the llm's version of of that and this is what seems to be changing a lot at the moment right because marketers use a bunch of tactics they work well everyone starts using those tactics law of shitty click-throughs kicks in uh and so then they diminish and then we need to find the next edge or like you know that these vendors like patch patch the vulnerability basically this is basically what we're doing is we're exploiting systems you know seo is just exploiting google systems um so i think there's like a whole thing there of like well how do i ensure that my brand my brand's information is chosen as a candidate and and the position i want um is communicated back to the user inside the llm like that that's how i'm i'm viewing it at the moment from like a marketing perspective yeah that's that's really helpful and i guess to that point there can be i i you mentioned this liam like or maybe it was ben like trying to get people to think more about like oh here's the prompt i want to optimize around and i know something that like people have talked about to me from various aspects is oh you need like a Reddit strategy to do great in AI visibility.

26:49And we were chatting a bit about this before the conversation came about, but I know you all have done some research, both earlier research on what actually gets cited in these LLM systems, but then more recent research around the weight and the importance of a Reddit strategy and how that surfaced visibly versus influencing some of that behind the scenes activity that you were mentioning before, Ben, do you want to just kind of help us understand some of the research that you've done there and maybe that Reddit piece or other things that kind of influence that citation that are relevant in relation to Reddit?

27:33Sure, yeah. So on the Reddit front, so obviously, as I mentioned, there's obviously the kind of like the fourth pillar of like AI visibility, the actual response, and there's, the reasoning or retrieval stage. And then we did a piece of research on the Chatwuti retrieval engine at the time, and it had allocated like almost a third of its retrieval slots for Reddit. So if it could find relevant threads for that topic or that query, it would basically inject that into the context window at the time. And then obviously as it gets those Reddit threads, let's say 30 % of its retrieval, let's say 60 slots 20 of them are allocated to reddit um most of those were actually rejected at citation time so they basically are kind of used to ground the model and formulate its thinking but often not actually cited in the final response and then there's also being so that's one aspect of it and then there's also uh also the long form reddit like amas questions and reddit training data is also used in the training itself uh i don't i think it's maybe around it's it's typically around the oral HF stage where it's like a reinforcement from human feedback.

28:48And that's basically golden training data for that. And if you actually see when we looked at this, there is traces of basically that is showing up in the waste, particularly from Chattanooga as well as Gemini. When we looked at basically the open source models, it's not like super present, but you can see it's there. So with those in mind, I think, you know, as I said, people say oh well quotations have dropped it's kind of like saying um you know it's one signal on the engine i would say but it's not everything um and ultimately if you want to as i said you want to um basically you know move the narrative for your brand or on in a certain direction you need to look at all sides the reasoning engine the retrieval side of it as well as the response and the the weights um so yeah i would say um it read it is still very much a big player yeah and maybe just to check my understanding here if i'm understanding what you're saying this would be like oh in that retrieval and query fan out stage let's say i retrieve 20 sources and 10 of them are from reddit saying a very similar thing and maybe one of them is from tech crunch talking about the same like it confirms the 10 reddit things it could be that the answer engine uses those 11 sources as confirming the same thing and that's what it's going to talk about but it doesn't cite the reddit things it cites the tech crunch thing because it's like i don't know uh for whatever reasons behind the scenes it it cites do i have the right understanding here exactly yeah that's exactly it so essentially it's like at that basically uh once we retrieve those those that um that set of, let's say, 20 sources that you mentioned, and let's say 10 on Reddit.

30:35There's a series of processes that go on, like someone that can be re-ranking versus the query, and then there's also some later research around basically domain authority and where ChattingT wants to send users itself. But yeah, all of those can basically compound to say, look, we've used Reddit to ground the answer here, we're confident to move forward with this one, but we haven't actually sided. That's exactly what's going on there. If you're like me, you need a good amount of help keeping your website updated, launching new websites, landing pages, etc. And you need an actual platform for your company, not just a builder of websites, but a platform where you can launch and continue improving your site.

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32:09That's framer.com slash practicalai for 30 % off framer.com slash practicalai. Rules and restrictions may apply. Ben, I want to do a quick follow-up on what you were just talking about, and that's kind of that relationship between retrieval and citation a little bit. And so it seems like they, like, intuitively, I might have thought that there was that, that there was a, you know, a positive relationship between them and that, you know, that retrievals would lead to citations. You just talked about the fact that, you know, that isn't necessarily the case. Could you talk, I mean, it seems like that's a significant thing.

32:53Like if you're, I would guess that if you're a marketer out there and going with that intuitive approach, that that would be kind of a substantial pivot that you'd have to make to kind of accommodate that. Could you talk a little bit about, you know, like what that means? What does it mean the fact that the retrieval and the citations don't necessarily line up that way. And Liam, I'd also love to hear what you have to say about that on the marketing side. So, yeah, so what I would say is, yeah, it is a big change because typically it's like, look, again, like, you know, traditional SEO would say basically, like, oh, query and index, we get something back and it's reasonably stable minus a Google core update.

33:32And now we have essentially, yeah, multiple steps, multiple query finance in that. And not only that, we have like re-ranking involved as well as like DLM, basically critiquing the information itself in this agree style algorithm that i mentioned so um yeah i think basically just to all just to kind of link back all the possible sources that citation time we talk to about about like basically citation time optimization to say like well how do you end up in the final citation um and then one of the things is obviously consistency like or a consensus like so if i have if i have this long tail query basically that one of the query finance we've been retrieved on what other supporting materials are out there that are likely to get ranked on that query to support it so it's like consensus we have a number of audits we do on this etc and then the other side of it is so once i pulled in that like even before so i pulled in that that um that set of sources that basically add consensus that will help and also you want to avoid saying well let's say um let's say there's some like anti-consensus information out there you don't want to you want to avoid that building up so let's say there's something about your brand where it's actually like negative, let's say, reviews, and it seems to be like it's published in multiple places.

34:42That'll be a good example. That's actually one thing we've built out a lot here is AI perception. And one thing we've found is actually quite interesting is that like, if there's blank space, you can almost publish, you can almost get anything cited. So for instance, let's say certain industries are very, very sensitive about publishing pricing. And we found this works like insanely well. If you publish numbers that even like yards stick directionally correct because there's blank space and the LLM doesn't want to hallucinate, essentially. So that's just a good concrete example of it. And similarly, if a competitor isn't talking about those, you can also do something similar to capitalize on.

35:18And the second thing is like, well, even when it basically perceives that information it's got from your brand, like how are you showing up in the weight? So it might have a very negative sentiment around your brand. and if it's looking at data from your brand and let's say, for instance, it's quite funny when we did the model rank analysis, profound as a really negative sentiment in the score and essentially that's going to impact them at citation time. And it's going to even come down to simple things like what is the name of your brand? So if your brand is like a word that is like, let's say, already an existing English word in the dictionary, for instance, that could be an issue or if it has negative connotations with it or it doesn't align with the overall, let's say it's a security platform and it's openly or something like that, that could have a negative effect.

36:11So there's multiple points there, both on the retrieval side, consensus as well as how the model is, basically the sentiment it has towards your brand and also what, so when these models are trained, coming back to the weight side of things, it's all about co-occurrence, right? So there's this attention function basically where it's basically going along looking at all the tokens in a certain space and it's associating with you. So if I have like, let's say, guardingtools.com, what are the other entities I've related to as I've gone through the training data? And then when it comes to citation time, it's like, well, that also influences like, okay, well, this, you know, we're talking about X.