Technical SEO
SEO Is Back. Just Not for Google
Most consumer brands treated SEO as a setup task, not a growth driver but that’s changing fast. As discovery shifts into AI systems like ChatGPT, Perplexity, and Rufus, the same SEO fundamentals now determine whether your brand ranks in Google or appears in the top answers in chat.
13 October 2025
8 min read
For most consumer product brands, SEO has never been a strategic pillar.
You’d optimise your Amazon listings, make sure your DTC store loaded quickly, run Meta ads with sharp creative, and maybe test TikTok or YouTube Shorts. That was the performance stack: agile, measurable, conversion-focused.
But SEO? That was usually a setup exercise. You’d hire someone to handle the basics like title tags, meta descriptions, a sitemap, and a few blog posts, then move on. Google visibility wasn’t ignored, but it wasn’t seen as critical either. For most DTC and challenger brands, it just didn’t drive the same short-term ROI as paid or social.
And for years, that made sense. Discovery happened elsewhere.
Social platforms drove awareness. Meta and Google PPC drove intent. Marketplaces like Amazon drove conversion. SEO was for publishers, travel sites, or enterprise B2B, not sunscreen, protein powder, or running shoes.
But the landscape is shifting, and fast.
Discovery is Moving to AI
Large language models like ChatGPT, Claude, and Perplexity, along with retail-specific assistants like Amazon Rufus or Google’s Shopping Graph, are quietly becoming the new discovery layer.
Consumers aren’t typing “best sunscreen for cyclists” into Google anymore.
They’re asking, “What’s the best sunscreen for long rides that doesn’t sweat off?” and expecting a conversational, credible answer.
And that answer increasingly comes not from ads or influencer content, but from models.
This means the same signals that help Google understand your brand are now being interpreted by a much broader ecosystem of AI systems that scan, parse, and learn from your product data, content, and brand footprint.
In this world, the fundamentals of SEO—site structure, language clarity, schema markup, and linked data—suddenly become foundational again.
But this time, it’s not about where you rank on Google.
It’s about how visible you are to the models shaping consumer discovery.
From Search Engine Optimisation to Model Visibility
At LMO7, we call this shift Model Visibility—the emerging discipline of optimising how your brand is interpreted and surfaced by large language models.
Think of it as the new frontier of SEO.
Clean schema, structured data, and coherent copy all become the scaffolding that helps models connect the dots: who you are, what you sell, who it’s for, and what signals of trust support it.
Traditional SEO optimised for ranking algorithms.
Model Visibility optimises for language understanding.
That means your website, product descriptions, reviews, social signals, and even your Amazon Q&A data all contribute to your discoverability in chat-driven environments.
When someone asks an LLM, “What’s the best sunscreen for triathletes?”, will your brand show up, or will a competitor’s structured content, clear claims, and consistent markup make it easier for the model to surface them instead?
Why This Matters Now
For brands that rely on discovery and awareness, this shift is huge.
The way consumers search, research, and buy is becoming conversational and context-rich. The experience starts not on Google, but inside AI interfaces like ChatGPT’s shopping assistants or Rufus on Amazon’s mobile app.
In these interfaces, the shelf is no longer a list of 10 blue links or a carousel of sponsored products. It’s a small handful of curated, high-confidence answers—often three or four brands at most.
If you’re not in that shortlist, you don’t exist.
That’s why SEO, or more accurately structured visibility, becomes critical again. It’s the connective tissue between your brand’s owned content and how it’s represented in the model’s knowledge base.
Where to Start
For most brands, the next step isn’t to rebuild your SEO strategy from scratch. It’s to reframe what it means.
Audit your signal architecture
Check how your site, product feeds, and content are being read by crawlers and AI models.
Get your schema right
Use rich product markup, FAQs, and review data to feed both search engines and AI systems structured, contextual information.
Unify your language
Ensure your product descriptions, brand story, and category language are consistent across every channel including Amazon, Shopify, and DTC.
Think in questions and answers
Structure content around the real questions consumers ask, not just keywords.
Track your model surface
Begin monitoring where and if your brand appears inside AI-generated answers and summaries.
This is the foundation of Model Visibility and the foundation of the next era of brand discovery.
The Takeaway
For years, SEO was something consumer brands could afford to treat as an afterthought. That era is over.
The future of discovery isn’t about ranking higher on Google. It’s about being understood by the models that shape what consumers see and trust first.
The brands that treat SEO as language model optimisation, not just website hygiene, will own the next decade of digital shelf space.
Because the new competition isn’t for search results.
It’s for answers.
You’d optimise your Amazon listings, make sure your DTC store loaded quickly, run Meta ads with sharp creative, and maybe test TikTok or YouTube Shorts. That was the performance stack: agile, measurable, conversion-focused.
But SEO? That was usually a setup exercise. You’d hire someone to handle the basics like title tags, meta descriptions, a sitemap, and a few blog posts, then move on. Google visibility wasn’t ignored, but it wasn’t seen as critical either. For most DTC and challenger brands, it just didn’t drive the same short-term ROI as paid or social.
And for years, that made sense. Discovery happened elsewhere.
Social platforms drove awareness. Meta and Google PPC drove intent. Marketplaces like Amazon drove conversion. SEO was for publishers, travel sites, or enterprise B2B, not sunscreen, protein powder, or running shoes.
But the landscape is shifting, and fast.
Discovery is Moving to AI
Large language models like ChatGPT, Claude, and Perplexity, along with retail-specific assistants like Amazon Rufus or Google’s Shopping Graph, are quietly becoming the new discovery layer.
Consumers aren’t typing “best sunscreen for cyclists” into Google anymore.
They’re asking, “What’s the best sunscreen for long rides that doesn’t sweat off?” and expecting a conversational, credible answer.
And that answer increasingly comes not from ads or influencer content, but from models.
This means the same signals that help Google understand your brand are now being interpreted by a much broader ecosystem of AI systems that scan, parse, and learn from your product data, content, and brand footprint.
In this world, the fundamentals of SEO—site structure, language clarity, schema markup, and linked data—suddenly become foundational again.
But this time, it’s not about where you rank on Google.
It’s about how visible you are to the models shaping consumer discovery.
From Search Engine Optimisation to Model Visibility
At LMO7, we call this shift Model Visibility—the emerging discipline of optimising how your brand is interpreted and surfaced by large language models.
Think of it as the new frontier of SEO.
Clean schema, structured data, and coherent copy all become the scaffolding that helps models connect the dots: who you are, what you sell, who it’s for, and what signals of trust support it.
Traditional SEO optimised for ranking algorithms.
Model Visibility optimises for language understanding.
That means your website, product descriptions, reviews, social signals, and even your Amazon Q&A data all contribute to your discoverability in chat-driven environments.
When someone asks an LLM, “What’s the best sunscreen for triathletes?”, will your brand show up, or will a competitor’s structured content, clear claims, and consistent markup make it easier for the model to surface them instead?
Why This Matters Now
For brands that rely on discovery and awareness, this shift is huge.
The way consumers search, research, and buy is becoming conversational and context-rich. The experience starts not on Google, but inside AI interfaces like ChatGPT’s shopping assistants or Rufus on Amazon’s mobile app.
In these interfaces, the shelf is no longer a list of 10 blue links or a carousel of sponsored products. It’s a small handful of curated, high-confidence answers—often three or four brands at most.
If you’re not in that shortlist, you don’t exist.
That’s why SEO, or more accurately structured visibility, becomes critical again. It’s the connective tissue between your brand’s owned content and how it’s represented in the model’s knowledge base.
Where to Start
For most brands, the next step isn’t to rebuild your SEO strategy from scratch. It’s to reframe what it means.
Audit your signal architecture
Check how your site, product feeds, and content are being read by crawlers and AI models.
Get your schema right
Use rich product markup, FAQs, and review data to feed both search engines and AI systems structured, contextual information.
Unify your language
Ensure your product descriptions, brand story, and category language are consistent across every channel including Amazon, Shopify, and DTC.
Think in questions and answers
Structure content around the real questions consumers ask, not just keywords.
Track your model surface
Begin monitoring where and if your brand appears inside AI-generated answers and summaries.
This is the foundation of Model Visibility and the foundation of the next era of brand discovery.
The Takeaway
For years, SEO was something consumer brands could afford to treat as an afterthought. That era is over.
The future of discovery isn’t about ranking higher on Google. It’s about being understood by the models that shape what consumers see and trust first.
The brands that treat SEO as language model optimisation, not just website hygiene, will own the next decade of digital shelf space.
Because the new competition isn’t for search results.
It’s for answers.