Dr Anna Harrison, a behavioural scientist and the founder of RAMMP, a brand trust diagnostic platform, has helped more than 900 businesses strengthen how customers engage with them and recently released a free AI plug-in that runs inside ChatGPT and Claude, showing business owners in real time where trust breaks in their buying journey.
Her argument is blunt: visibility inside AI tools costs little relative to what it returns, the early movers gain an advantage that compounds, and structured content surfaces fast. Her own site appeared in ChatGPT answers within days.
In this article, she explains the business case for moving now, what to build and in what order, how AI tools find and prioritise content, the discovery files, the differences between platforms, the role of off-site sources, and who inside a business should own the work.
For a business with limited time and budget, SEO and AEO compete for the same resources. How should a resource-constrained company balance the two?
It's a hard question, because it's not really a balance. It's two separate things. I'd probably double down on AEO for a sprint, put a stake in the ground and get it all done. Even though you're shipping 40 to 60 pages to your website, which sounds like a lot and in the old days would have taken half a year, you can get this designed and shipped in far less time.
If your focus right now is on SEO, I'd pause that for a sprint and get this up and going, or run them in parallel for a month if it's an industry where you can't lose the SEO traction. You've got a solid case to make, because the earlier you do it, the faster you'll start showing up. If you think about the nature of LLMs, the unit of consumption is a web page, and they consume all those pages into a giant soup. The earlier your page goes in, the more chance it has of being propagated throughout, and the longer it's in there, the more it works for you.
Two terms come up repeatedly in this work, ontology and knowledge graph, and they are often used interchangeably. Is an ontology the same as a knowledge graph?
Close, but not identical. An ontology is the schema, the rules defining what types of things exist (Standards, Definitions, Methodologies, Decision Controls, Comparisons) and how they relate to each other. A knowledge graph is the instance, the actual filled-in network of those types with your specific content.
The ontology is the architecture. The knowledge graph is the building. When I built RAMMP's, I defined the ontology first, then populated the graph. Doing it in that order keeps the structure clean. You can build your own ontology here.
Building the pages is one task; getting AI crawlers to actually read them is another. How do you get the LLMs to read these pages?
You don't trigger them. You make the pages easy enough to find that they self-trigger. Three mechanisms do the work. The page is on the public web and crawlable, with no auth wall and not blocked in robots.txt. The JSON-LD schema in the header signals that this is structured content worth eating. And llms.txt points crawlers at it.
Beyond that, LLM crawlers run continuously. Anthropic, OpenAI, Perplexity and Google all run their own, and there's no submission process. The fastest acceleration is to be referenced from a high-authority source such as Wikipedia, Reddit or a trade publication. That pulls crawlers in faster than waiting for them to find you organically.
In traditional SEO, page depth and sitemap position mattered. Are pages that aren't in the sitemap still accessible to AI tools, and do LLMs prioritise pages higher in the hierarchy?
Yes, fully accessible. LLM crawlers don't rely on sitemap position the way old SEO crawlers did. They prioritise three things: a well-formed JSON-LD schema in the header, technical precision in the content, and clean cross-references between pages.
A deeply nested page with crisp structured data will outperform a top-level page full of marketing copy. Hierarchy matters less than schema quality.
There's a new method gaining importance for LLM discovery, the inclusion of an llms.txt file in a website's root directory. Can AI engines crawl llms.txt, and what is your view on this?
Llms.txt is an emerging proposal, not a settled standard. Anthropic uses it. OpenAI hasn't formally committed. Google has been quiet.
Treat it as cheap insurance, not a load-bearing strategy. Deploy it because the cost is near-zero and the LLMs that respect it will benefit. The real work is in the structured pages and the JSON-LD schema, and those work regardless of whether llms.txt becomes the standard. The pattern across the AI ecosystem right now is that well-structured content wins, regardless of which discovery file the crawler reads first.
Some pages end up serving both humans and AI crawlers, which can mean similar copy in more than one place. Is there a risk of duplicate content hurting SEO or AI authority when a page serves both audiences?
For some of the pages inside the knowledge index, we've recognised that a page is customer-facing and worth deploying, so we've added the design elements and linked it. There are some pages that serve two purposes.
As you go through this process, if some of your comparison pages or decision control pages double up with something customer-facing, you put a bit of extra energy into that page. Make it a human-facing page, but make sure you have all your definitions and those elements included in the header, with a JSON-LD schema that ties it back to the knowledge graph.
Reviews, forum threads and other user-generated content sit outside a brand's control. How does user-generated content affect your ability to control the AI narrative?
I think it's orthogonal to this issue. You have no control over user-generated content, and it's also not structured data, so its weighting as it's consumed by the AI carries far less priority than something structured in this way.
Most of this discussion centres on ChatGPT, Claude and Perplexity, but Google's Gemini sits behind AI Overviews. Any commentary on Gemini compared with the others?
Gemini is competitive on multimodal tasks, it reads images and video well, and it has a meaningful advantage if you're already inside Google Workspace. For research-style queries it's solid. Where it lags is depth of reasoning on complex business problems, and Claude has the edge there as of right now.
The bigger strategic point for AEO is that Gemini powers Google's AI Overviews, the AI-generated answers that now appear at the top of Google search results. So even if you never personally use Gemini, your AEO work is being read by it every time someone searches for you in Google. Treat Gemini visibility as table stakes, not optional.
How do you figure out what keywords and topics are being searched for inside AI?
This comes back a little bit to SEO. As a practical example, if you search for "best wineries in New Zealand" in Google and scroll down to the "people also ask" questions, that's your starting point. I would surface those questions straight on the website.
We have a set of query-capture pages, and these come from the "people also ask" questions. We take the question from Google, build a page around it, and integrate it with the knowledge schema we've created. Everything connects. We drop a new node into the graph, connect it back to the other nodes, and ship it.