Thanks, everyone. AI is actually changing the way people interact with your brands. At ai12z, we make an AI platform. That platform helps brands to create intelligent conversational experiences. With Magnolia, we connect our platform so that we can connect our platform to the AI platform. we can use it as a truth of source. But ai12z creates digital assistance as well as AI search and personalization. We also provide a suite for a GEO in AIO so that brands can know, are they content, is it being cited, discovered, and can they use, can that content be measured as well? So the way people search today has changed. They used to use Google, but now they're using ChatGPT, Gemini, Cloud, Perplexity. And so with Magnolia and ai12z, we've created a set of tools to help brands adapt. Because adaption is really required because they're seeing things around their Google SEO change and drop. So in ai12z, you're either part of the answer or you're invisible. There's nothing, not like what you used to have with Google, are you on that first page? It's your, you're, you need to be cited. And so there's techniques that you can do to help perform that. And we're going to talk about that. So there's three-way brands show up in AI. GEO, AEO, and AIO. How AI builds an answer is the same way ai12z builds an answer when you're using our RAG system. It collects data from multiple sources, collects the snippets, and creates an answer. And it includes in that answer where the answer is. It includes in that answer where did it get the information from, so the citations. So how to show up in ai12z is part of what this GeoToolkit can help you with pretty dramatically. So some of the basic things are shown here. But our tools, like just in our URL analysis, it creates a 36-page presentation to you to help you understand how does this page actually refer to. So some of the things are pretty basic. Do you use H1s and H2s? Are your H1s and H2s formulated as a question and answers? Does your answers show up in the top 30%? Are you creating structured JSON-LD? Because if you're creating structured JSON-LD, it's harder for these AI engines to understand. It's harder for us. So we can see dramatic difference when you have JSON-LD in the content. And whether or not... One of the core things is to be able to measure what's working and what's not working. So now you get discovered. So someone's on ChatGPT and you're actually cited in the content. And then they come back to your website. It's almost like in the days... Basically, in the early 2000s, when a search came about, everyone added search to their website. Your brands and the people that are coming to those brands are expecting a more conversational experience. They're expecting that they get the answers immediately. It helps people navigate to the information on the website, even though it answers the question either in a bot or in an AI search. So we connect with Magnolia CMS in three different ways. We have a Magnolia connector that is... Magnolia is the source of truth for the content for the reg. The reg is the question-and-answer engine. So as you make changes in Magnolia, it automatically gets updated in our vector database. So a vector database is different than what you would have with a search in the traditional search, where it uses keyword analysis. In a vector database, it's meaning-based, which means you can ask questions in any language and get the answer in the language that the person asked the question in. The next thing that we do is we can integrate with Magnolia through an integration. So often, you have sites that are generating structured data, like a product company, or you show events on your website. It's often better to call an integration with the LLM creating the structure to do search. So if it calls through GraphQL, it can actually create the entire query to sort and bring back the relevant results, and then show those results inside of a carousel. The third way that we integrate is that we have management APIs that some of the things that you're going to see in the live demo, we can provide those information back to Magnolia. So someone that's a Magnolia can actually see what's happening with the experience through their AI chatbot and their digital assistants. So websites are going to change pretty dramatically in the future. So when most people think of a chatbot or they think of AI search, they don't think of the entire page becoming the digital assistant. And we're now providing pages that are the digital assistant. What we've done and worked with Magnolia is when we make a change, so this is dynamically changing in real time, we can call Magnolia components to show up on the page. AI 1 to Z also has landing page controls. So things like forms, carousels, HTML widgets, all of those can change on the web page and creating a really dynamic experience, reducing the time that people take to get them to the information that they're looking for. One enormous advantage that AI 1 to Z has when you think about AEO, GEO is that we actually have all the natural language questions in our system. It's like a feedback loop. It helps us to understand what's important that people are asking there. And that's what we're going to show you in the GEO a little bit later on. But what ends up happening is you don't even know why you're not showing up on these engines. So to have a tool that actually can talk to the engines and find out are you being cited on there so that we have ability to have citations monitoring in the system. We also, through MCP, integrate with Google Analytics. So you get a real-time feedback. So we build a chatbot for Google Analytics that you can use to understand all sorts of things on your campaigns. All the things that you can do in Google Analytics, we expose. But we then use that data in our analysis. So now we're going to talk about really quick stuff. So a CDP is Customer Data Platform. And it's used for personalizing the experience. So we're going to show you an example where the user comes to this page and thinks it's a 55-year-old person. It's going to show them investment opportunities. And so, but the trouble is, is when that user comes there, you notice that the page is about investment as well because a CDP told the website. But the user is actually interested in an auto loan. And so now when the person asks the question, it's the first time that we really know what the intent of that user is. And notice that a lot of things have changed. The website's changed. We've navigated them to the right page on the website. The bot has changed. It's a multi-panel experience. And now they're on auto loans. And so the next thing we're going to talk about is HTML widgets, Vibe coding, and calls to actions. So HTML widgets, you can go into AIR1 to Z and create an HTML widget. It's part of an integration. And then in here, you're seeing a mortgage calculator where I'm asking, can you calculate a 25-year loan? It's going to bring up this HTML widget that we created. It's already selected 25 years. If I told it the home price, it would have filled in that information. It's a dynamic widget where you can make changes. And now when they calculate payment, this is the calls to actions. So it isn't just about an AI system answering people's questions, but it's always driving people to the next step, getting them to a call to action. The next thing is going to be about GeoSearch, Google Maps, e-commerce, and Shopify integration, and carousels. So when we're doing the GeoSearch, what you're going to see is that it's the navigation search. It's used in Google. It's a different kind of Geo. So when you click on locations here, notice that it's going to come back with a carousel. And guess which one it's coming back with? Atlas Zurich. And when I click on, and you can see the next one's Atlas Munich. It's the stores. When I click on Get Directions, you're going to see that we're going to call the Google integration, and we're going to show from Zurich to exactly where we are right at this very second. Because it knows your geolocation of something that you're doing. Now we're going to talk about using Shopify. And so when I click on Helmets, notice that these are carousels. Carousels are really important widgets where they can bring back information, structured information from backends. And that structured information then can be used to control this. Now these are the landing page controls and directives. This is, again, where we interact with Magnolia. So the landing page here is the persons on the page, we think they should be talking about Brampton, which is all this information is about Brampton. You can see the rental properties is about Brampton. And so this is, again, a carousel information using our landing page controls for that. And then when we look at the maps, it's going to be the maps for Brampton. But then now we're going to get the first experience that the user is going to interact with the page. And that is they type something into a search bar. So they're going to say, tell me information about Toronto, which is going to launch the bot. It's going to answer the question about information about Toronto. But it sends a directive to say, go change everything on the page. Because the person's interacted in Toronto. So the map changed. Now the HTML widget, which this is about Toronto, is all changed about that information. The rental properties are all about Toronto. The AI knowledge box is the quickest way to get somebody to go. You literally can go live in one day. Imagine somebody doesn't want to replace their search. They're still scared about AI. But what you find is that this conversion rates in search is so dramatically better. So you get this AI overview. You click on this. Now you have links. And you also have calls to actions. Now we're going to talk about GEO. This is really the gold of things that people want to do today. And we've built a really impressive toolkit for doing GEO, AEO, and GEO discovery. So the first thing that you're going to see is that this is your dashboard of how your site is doing in AIO, GEO. And it's based on all of these reports that you see on the left-hand side. It analyzes everything that you run, which is way more sophisticated than this. So this right here is the question-and-answer engine. This is where we take all your questions and answers. We analyze the information. We know when we weren't able to answer. What's the top 50 question themes? The answer quality. And we're reporting back to you suggestions on what you should do to generate that. As time goes on, you're going to do a question-and-answer analysis trend. So you keep on running like every month a question and answer analysis. And then you look at the trend that's happening. And this is going to tell you over time what's happening with your site. The next thing is URL analysis. This is where you're going to look at a URL, analyze it, understand all the things that you could be doing better to make that particular web page better perform for GEO and AEO. And there's a lot to it. So then the next thing that you need to do is the keyword visibility. This is taking the concept that you actually didn't install AI went to Z chat widget. And you want to just create keywords and use that. Like you downloaded all the keywords from Google and you went and processed it. We would have still ingested your site so that we can understand how every question and answer is answered. Then this is your AI readiness report. It looks at all your robots, your site map, how fresh is your content. And it gives you a score that's associated with that and starts making recommendations on things that you can do. Next one is going to be around the AI social. So it's going to look at your AI footprint and understand it's not just about your website. It's also about being where you're found. Like are you on Reddit? Are you on LinkedIn? Are you on YouTube? And all of those things make a difference because AI needs to understand are you really a valid person. This is Citation Monitoring. This is really powerful. You put in the terms that you would like to be found in the particular search engines like ChatGPT. And then what ends up happening is you always put your competitors in there. So not only does it test for your competitors, but it also is going to test you and tell you how well you're doing. It's going to also give you an SEO score because it checks Google. The site-wide analysis is this is where we ingest content into the system. And when we ingest that content, we then take that content and analyze it and tell you where you might have problems and things that you could go fix on ingesting it. The action plan is a summary of all the things that it's seen. And then in this action plan, it's suggesting to the marketing team things that they can go and do. As you see, some of these reports are pretty long. So the action plan kind of summarizes things to go do. This is kind of a cool one for all the agencies here. We have something called a prospect snapshot where you put in the information about your customer, your name as a partner, and you say submit job. It creates a 10-page kind of a teaser. It's not meant for GEO, AEO, but it's a teaser to get people into the next conversation. Now this is real time. This is data coming in for you're looking at. Here are all the answers that are happening in real time. The other one, content quality, was the content quality that came in. We score everything. You can go sort on filters to say show me all the quality of content that's below a certain thing. You can hit the analyze button. You can always answer the question. Why did they answer the question the way it did? Because we know all the metadata that we use to answer the question. And then I think that's it. Thank you. Any questions? Yes. There's someone back there. Hello. Okay, so you covered a lot there. And one thing that I'm curious about in particular to hear more about was that customer data platform consideration. Because that's one of the key items that we're looking to add to our MarTech stack. Yep. So, okay, the customer data platform, it has an initial impression of what it thinks the customer wants. Your integration is then refining that in some way, right? Like, it says, oh, actually, based on the question that the customer is asking in your intent box, we actually understand that they're looking for a loan instead of being interested in wealth management. But one of the purposes of the customer data platform is to be the single source of truth of who the customer is. So I'm curious how your widget interacts with that. Like, how does that maintain its single source of truthdom so that the next time they come back, it knows more about what their actions were? Correct. Yeah, correct. And so the point is that when the page opens, the control is being passed that customer data profiler information. And then we're going to go set the – it's a multi -panel interface for seeing the welcome screen. So we're going to set that. The next thing that we would do is that we can then write that information back out to the customer data profile. So it knows the next time that they come, the person was actually interested in an auto loan. And so they come again, we're probably going to see an auto loan experience as well. So next question. What determines the data quality score of the results? So it's AI algorithms that's analyzing the data score. So we are using techniques that we develop with our SEO consultants to determine what that is. So there's a lot of Python running in the background to cluster content together. And then it's being used to then analyze, to make a data quality score when you're doing the QA analysis. When you're looking at just a raw piece of content, we analyze it like if it's a URL, what are the things that we would expect to see in that webpage? For example, is it – are you actually answering the question in the top 30% of the content? Do you have structured content with H1s, H2s? Do you have bulleted lists? Do you have tables? Do you have lists? All things that are really good for an AI engine to go read and understand. And so that's what basically adjusts the score.