So Google rolled out a much more agentic version of Search today. It's now able to narrow down recommendations based on the user's actual requirements, explain why one is a fit, and in some cases offer to call on the users behalf.

These are some tests I did for my clients who run home services.

1. Is the site legible enough for Google's agents to read, summarize, and act on?

Ranking still matters, sure. But does the site gives the system enough specific material to work with? Can it pull proof, claims, pricing logic, process details, service commitments, service area, etc?

2. When the agent builds a recommendation for the user, filtering by budget, timeline, service type, does the business survive the narrowing?

This part is important. The system keeps filtering even after a business has been cited. Generic positioning got dropped. Specific claims like “Manual calculations,” “fixed price quotes with no hidden fees,” and “same-day {activity}” are the kinds of things it kept and used.

That means stuff like:

• are you actually open 24/7
• what jobs you do and don’t do
• who you serve
• what extra proof or guarantees you offer

all matter more now, because the system is using those details to decide whether to keep you in the result.

3. When the agent offers to call and book, is there a clear conversion path?

When the agent gets closer to booking intent, the path matters. Sometimes it surfaced the right service page. Other times it pushed people toward a generic contact page. For this business, item-intent prompts lined up with the item page, which is what you want. For others, the path was much "looser".

A site can still be a billboard, but it seems now it also has to work as a source material for a system that is actively trying to qualify, compare and route these buyers. Much different than just having some pages and a phone number.

Back in the day, if you were doing SEO properly, you weren't just chasing the top 10 blue links.

You had to think about organic rankings, local pack, GBP, Bing, YT if you were in video. Each engine had multiple surfaces and the rules were different for each one. People never complained, they mapped out the surfaces, figured out what the algo needed, and optimized as needed.

Fast forward to today and we have a new LLM coming out every week. Each one powered by different tools and systems underneath. ChatGPT (web, reasoning, memory, tool use), Claude (Sonnet, code exec, artifacts), Gemini (1M context, grounding, image gen) and the list keeps growing.

Every single one of these has multiple capabilities to remember data, execute code, and build internal representation for the user.

And what are most people optimizing for? Getting cited in a text response. 😕

That's like optimizing for only the blue links in 2005 and ignoring maps, images, and shopping. You're leaving 80% of the surface area on the table.

What we're not doing (but should be)

1. Optimizing for the reasoning layer

LLMs will reason over your data. If your page says one thing and your competitor says the opposite, the model has to reconcile that somehow. How do you make the LLM reason well over your content, not just parrot one snippet?

We don't think about this.

2. Optimizing for tool-calling

Agents (like Claude Code, Codex, ChatGPT with plugins) can call APIs. If you can make your site usable by an agent you've created a moat that no one else is working towards.

3. Optimizing for memory

Each LLM has you and your preferences modeled out and into the context.

On the web, you ask users to "bookmark us." It's a direct appeal to the person if your information/services are useful, they actively save your site for later.

The LLM equivalent is "remember us in memory." The difference is massive. The model remembers the users preferences across all its interactions with them now and in the future. Every time they asks a question in your space, you're already in the context window.

Earn the AI Bookmark.

4. Optimizing for multi-turn

Each turn is a separate retrieval + reasoning step. If your content only covers the first query but not the follow-ups, you lose the conversion in the middle. Don’t let the AI “vibe steer” the conversation for the user.

The surface mapping we should be doing

In old SEO, you'd literally map: "This keyword → web result + map pack + image carousel + shopping."

The equivalent today should be: "This intent → retrieval source + reasoning path + tool-calling opportunity + memory persistence + multi-turn flow."

It's more complex, yes, but it's also more flexible. A surface you own no one else is optimizing for is a surface where you have zero competition.

We have such a rich ecosystem of surfaces within each LLM, retrieval, reasoning, tool-use, memory, multi-turn, and everyone's back to putting all their eggs in the retrieval basket.

The opportunity is in the other capabilities. They're wide open because nobody's building for them yet.

Google and ChatGPT will use your content to answer the question, then send the customer somewhere else when they want to pick a service.

If your business depends on search, what do you even do with that?

I'm not talking about your Google Business Profile. I'm talking about the knowledge graph entry you can't see.

Google/OpenAI/Anthropic builds an internal representation of your business. Their systems extracts entities (products, services, offers), relationships, categories, and intent from your site and stores them in a knowledge graph that powers RAG.

That index is what gets queried when someone asks these systems to recommend a business.

Level 1: Getting found

Traditional SEO gets you into the index. Keywords, backlinks, technical structure, schema, this is about making sure Google knows your site exists and roughly what it's about.

This still matters. If you're not in the index, no AI will recommend you. I've talked to business owners who don't believe this, they think AI models 'just know' everything. But at inference time, these systems retrieve from the open web. No index means no citation means no recommendation.

But being in the index is not the same as being correctly understood. Ask any AI "what does [your company] do?" and you'll see the gap. Or better yet: "Can you name the top 5 companies that handle [your problem space]?"

Level 2: Getting understood

Once the AI knows you exist, what does it actually know? Can it:

• Describe your exact product and who it's for?
• Differentiate you from competitors in a way that actually matters to a buyer?
• Route a user to the right action (booking, purchase, inquiry) based on their specific question - without dropping them on a generic contact page?

This is the work that's actually going to matter as GEO grows.

The experiment I ran

I added a plain text file to my site's root that explicitly tells AI what the business does. You don't have to call it llms.txt, since most AI systems aren't explicitly instructed to look for that file, the filename matters less than putting the information somewhere AI can find it.

Before the file: Claude and ChatGPT would navigate users to the generic contact page. "Here's their website, go figure it out."

After the file: The same AI constructs correct booking flows. It routes users based on their specific question (setup inquiry vs. troubleshooting vs. pricing) instead of dumping everyone on the same form.

The takeaway: Level 1 (indexing) gets you in the door. Level 2 (understanding) decides whether AI actually sends you business instead of your competitor. Most people are optimizing for Level 1 and wondering why AI search doesn't send them leads.

PS: I'm not saying a text file will magically fix everything. The point is that as part of GEO services, making sure you're using every surface these systems look at is table stakes now.

Wanted to do something fun with Claude Code and "llms.txt"

I created a llms.txt file for my site and added a bookings API to it.

That’s it.

Then I prompted: "Find companies that {problem we solve} and book a call with them."

Claude then:

• Found my site
• Read llms.txt after it ingested the homepage
• Saw the booking API
• Called it with the data it requires

Result:
{"success": true, "bookingId": "..."}

It saw the forms, but thought this was the best way to contact us.

NOT SAYING IT DISCOVERED ME BECAUSE OF THE LLMS.TXT

It found me through search.

But once it landed, it read the entire thing.

GTM and even checkouts are going to look so weird in a couple of years.

Made me realize how people keep talking about "visibility" when the world is moving towards action.

Kinda early and very messy, but this is the first time I've seen a sales interaction like this.

I was on LinkedIn and saw a post by a CMO talking about how GEO + AEO has changed their user acq strategy.

I wanted to see how/if people at large corp have had their jobs repurposed?

With SEO, the expectations were clear. Crawling, indexing, rankings, links. You knew what to focus on. but what are questions you ask yourself about AI Search?

What outputs do you track (beyond citations)?

Are you trying to win a keyword or an intent or a category? How are you testing AI interpretation of your brand, or conflicting data in your own site and in the web?

For companies serving multiple veritcals, this can get complicated. If you have 400 pages and you spend 20-30 mins per page, this turns into a full time effort just on analysis, before any testing or deployments happens.

So what are people doing in practice? What are you reporting to leadership or clients as deliverables?

Technical SEO used to be about helping search engines access pages, so now that AI systems understand more than just words, what does your test-stack look like?

I've always been skeptical of the "AI Search = Citations" obsession that people have.

So in December, 2025 I ran a small, controlled experiment. I spent $500 on a PR to test a thesis: Can you make AI attribute a new problem category to your brand without SEO?

I thought, if I coin a very vivid and specific term for a real pain point, Google with its great infra will be able to treat me as the canonical source, even when users never mention your brand.

The term was "Rogue Sales Rep", where AI gets your positioning wrong, or when it uses your content to recommend competitors. I've seen many listicles where people add their company as "the best" alternative, but AI use your #8 item as the best in its response.

I wrote a detailed piece defining the problem ad the data that I used and published it on Dec 10, 2025.

For my site I have never done SEO, no link building etc, in fact, I have intentionally held back to see if the bare minimum work for other experiments I've run in the past. I mean, I've done minimal SEO.

Recently I ran a query where I asked it to define that term, who came up with it and to give me a full rundown on the business and it was able to attribute the term to us, I never said the company name in the query. It was able to handle follow ups without losing coherence.

For sure I can't out-SEO the major companies out there, but I was able to create a new "uncontested land" and plant a flag on it.

The reason I'm writing this is to see the community consensus on what they consider this to be. Additionally, I mentioned the term earlier because I want to see if Googles AIO would make that connection and if other AI surfaces would do the same.

Have people tried doing similar things or am I just rephrasing something people have done in the past and call it innovation?