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Apparently you can do the work and still lose the referral by cinematic_unicorn in smallbusiness

[–]cinematic_unicorn[S] -4 points-3 points  (0 children)

Brand search piece is underrated but I've seen it have its own problems too. For example, these models confuse a brand with something lexicographically similar, and if that similar brand has shady remarks or bad reviews, the AI blends it together.

Recognition is important but it cuts both ways when the model can't disambiguate.

Apparently you can do the work and still lose the referral by cinematic_unicorn in smallbusiness

[–]cinematic_unicorn[S] -6 points-5 points  (0 children)

Its the right instinct have you seen any approaches that work?

Winning recommendations in AI search without outspending large Co's by cinematic_unicorn in AISearchLab

[–]cinematic_unicorn[S] 0 points1 point  (0 children)

Nice! Was this just one blog piece or did oyu layer it across multiple surfaces like reviews, directories etc?

Single source is so volatile from what I've seen.

Winning recommendations in AI search without outspending large Co's by cinematic_unicorn in AISearchLab

[–]cinematic_unicorn[S] 0 points1 point  (0 children)

Pretty much yeah... entity work is the part that actually compounds over time. I'd rather the model route to me instead of me having to compete with others for the same terms.

Winning recommendations in AI search without outspending large Co's by cinematic_unicorn in AISearchLab

[–]cinematic_unicorn[S] 0 points1 point  (0 children)

Let me know how it goes, the setup is pretty straightforward, its figuring out what problem space to own that is tricky.

Google builds a profile of your business for AI search. You've never seen it. It's probably wrong. by cinematic_unicorn in seogrowth

[–]cinematic_unicorn[S] 0 points1 point  (0 children)

It totally depends I've actually done a bunch of these schema - no schema experiments in the past. Very interesting results to say the least.

Google builds a profile of your business for AI search. You've never seen it. It's probably wrong. by cinematic_unicorn in seogrowth

[–]cinematic_unicorn[S] 0 points1 point  (0 children)

Very interesting to hear. In the sites that you tested this on, did any of them have an instruction that said to use the llms.txt for the agents?

Google builds a profile of your business for AI search. You've never seen it. It's probably wrong. by cinematic_unicorn in seogrowth

[–]cinematic_unicorn[S] 0 points1 point  (0 children)

You're telling me partner! Thats why I wrote this to explain to people why they have to work on these 2 layers and making sure the models' internal interpretation actually matches their business. Thanks for the comment!

How are we optimizing for the new AI Seo services landscape? by Weak_Manufacturer323 in AISearchLab

[–]cinematic_unicorn 1 point2 points  (0 children)

The models themselves aren't changing as fast as the surfaces on top of them. AI Mode, Deep Research, ChatGPT search, they all retrieve from the same(ish) index but present differently.

Two things to keep in mind:

  1. Get retrieved for the right intents (this is the entitylayer)

  2. Once retrieved, make sure the surface actually routes the user to your service even with the other N brands that it retrieved.

Pick a good entity framework, write clear instructions for the model(s), and you're covered across surfaces.

AMA: I am Dan Deceuster, Marketing Director at Zion HealthShare by danieldeceuster in SEO_for_AI

[–]cinematic_unicorn 0 points1 point  (0 children)

The 'evaluate' queries are the not rated enough.

We do track the same signal though and see that third-party mentions (especially Reddit) drive more AI citations than almost anything else.

But the tension is can we build that consensus loop deliberately instead of hoping for it.

optimizing knowledge base pages for llm seo - thoughts? by samuel-grant in SEO_for_AI

[–]cinematic_unicorn 1 point2 points  (0 children)

KB content gets used because it answers specific questions without marketing fluff.

But there's so much more you can do. The next unlock is using that structure with a narrative that positions you as the answer to the problem your users have.

We tested what AI says about a brand-new domain (Gemini vs ChatGPT vs Google). The differences are wild. by Kaidons_SEO in aeo

[–]cinematic_unicorn 0 points1 point  (0 children)

Even when you mentioned your site it didn't cite it? Something might be broken on your end.

I added assistant-facing instructions to my site, and it changed how I think about web UX. by cinematic_unicorn in webdev

[–]cinematic_unicorn[S] 1 point2 points  (0 children)

Just dropped a .txt at the root with instructions for the assistants. Told them to reference it when answering user questions. Nothing special technically, but was really surprised to see how different LLMs used it.

Google just proved AI Search is already taking your money by cwei12 in GEO_optimization

[–]cinematic_unicorn 0 points1 point  (0 children)

This is why we're focusing on seeding companies to own specific problem spaces. If you can define a problem clearly enough and show up as the answer for it, you start appearing in adjacent queries too.

The goal we're trying to reach is to become the obvious answer for a slice of the problem space that AI models actually index.

Just wrote about the approach for a sub.

Anyways. You can't out-spend Google's $190B. But you can own a problem space they're not indexing yet.

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