Digital marketing has always been about being found.

For decades we fine‑tuned websites for search engines, chased high‑value keywords, and optimized landing pages so that Google’s algorithms would reward us with traffic. That playbook served us well, but it was built for a world where search engines displayed lists of links and humans clicked through those lists.

In 2025 that world is fading fast.

Consumers are increasingly bypassing search bars and taking their questions straight to large language models (LLMs) like ChatGPT, Claude, Gemini and Perplexity. When ChatGPT can synthesize research, compare products and make recommendations in a single conversation, your brand either appears in the answer or it doesn’t – there is no second page.

As marketers, we are staring at the dawn of a discovery layer controlled not by ranked lists but by AI systems that retrieve information, synthesize it and deliver a single recommended answer. Understanding how this layer works is now critical to building visibility, trust and revenue in an AI‑first era.

Below is a human‑centred guide to the LLM discovery layer –  what it is, why it matters, and how marketing must evolve to thrive within it.

The New Front Door: Discovery Happens Inside LLMs

Search is not disappearing overnight, but it is no longer the default starting point. Adobe notes that generative AI platforms such as ChatGPT, Claude and Perplexity have become the new front door to brand discovery, summarizing and recommending content rather than simply indexing it.

Consumers already rely on AI to handle a significant share of their information needs. Bain & Company found that 80 % of consumers use AI‑written summaries for at least 40 % of their searches, while Gartner projects that brands could see a 50 % or greater decline in organic traffic by 2028 as customers move away from traditional search.

That shift is accelerating.

Today, ChatGPT handles around 37.5 million search‑like queries per day. Perplexity AI alone processed 780 million queries in May 2025 and its usage has been climbing 20 % month over month. Meanwhile Google’s own AI Overviews now appear in more than half of all Google searches, and when they do, websites typically see click‑through rates decline by 15-35%. (Source)

So, if your information is not surfacing in AI answers, it might actually never reach your customers at all.

Why Traditional SEO is Not Enough Anymore

Search engine optimization rewarded content that was well‑organized, keyword‑dense and supported by backlinks. But, LLMs operate very differently.

AI models retrieve, reference and generate answers from memory, context and trusted sources – they don’t just rank pages. And therefore, search rankings alone won’t get your brand found in ChatGPT or Claude. Traditional tactics like keyword stuffing or chasing backlinks now have diminishing returns as AI systems care less about popularity and more about structure, citation and persistence.

Dev Chatterjee of Inbound Medic makes the point bluntly –  websites built for compliance or aesthetics are often invisible to language models because LLMs don’t browse –  they parse structured data, entity relationships and semantic clarity. Without schema markup and clear headings, they cannot understand what your business does and so it won’t surface you in their answers.

This is the crux of the shift – we have moved from search to retrieval. SEO, AEO, even the early attempts at GEO all fall short if your content is not machine-readable, properly cited, and consistently reinforced across the web.

What LLMs See When They Crawl Your Site

Understanding how LLMs ingest content is the first step toward being retrieved. What these models look for are –

• Structured schema that defines each section of your site (e.g., Service, FAQ, Terms)
• Semantic headings that express topic hierarchy and intent
• Entity mappings linking your terminology to global knowledge graphs like Wiki data
• Clear relationships between your company, products, people and processes

If those elements are not present, you simply don’t get retrieved.

Beyond your own site, LLMs also learn from off‑domain sources like Wikipedia, Reddit and public forums. These AI systems crawl and cite content from across the web, and inaccuracies in those third‑party descriptions can misrepresent your brand and hence, part of your strategy should be  to ensure that third‑party information like Wiki data entries or product listings  are accurate and properly structured.

Visibility is an Infrastructure Problem, Not a Campaign

A common misunderstanding among marketers is that we can optimize our way into the AI layer with a few tweaks. This really does not work.

Visibility inside LLMs is not the result of some minor adjustments – it’s the outcome of how your entire digital footprint is built and maintained and that requires more than just a few tweaks.

Static, PDF-heavy websites or rented platforms, for example, often remain invisible because language models don’t crawl in the traditional sense. To show up, your content has to be machine-readable – wrapped in schema, organized in modular chunks, and anchored to credible sources.

When done right, each page becomes a durable trust node for AI systems – something that compounds over time rather than vanishing when a campaign ends.

Tim de Rosen’s AIVO Standard formalized this thinking. AI Visibility Optimization (AIVO) aims to make content retrievable, referenceable and persistent across model versions through five pillars –

1. Structured citation density
2. Schema markup and entity resolution
3. Persistent reference nodes (like Wikidata and PDFs)
4. Prompt pattern seeding
5. LLM‑compatible metadata and author credibility signals

In other words, AI visibility is more like technical SEO for machines. It requires investments in data architecture, not just copywriting.

Practical strategies for marketers

So how can marketing teams adapt to this new discovery layer? Below are some practical steps that can help you navigate this –

Design for Retrieval, Not Just Readability

LLMs don’t read like humans –  they skim for structure and therefore, the way you frame and format your content matters more than ever.

Think of headings and subheadings as signals. When they pose specific questions and are followed by clear, direct answers, models can lift those passages with confidence. Break content into bullet points, numbered lists, and FAQs. Add short definitions, examples, and clarifiers. In practice, this turns your site into a library of modular, quotable blocks rather than long walls of text.

Equally important is how you group ideas.

Instead of chasing keyword clusters, build around intent clusters. Create ecosystems of pages that tackle related questions across the buyer journey – from top-level explanations to deeper how to’s, and context-rich case examples.

And rather than aiming at broad head terms like “email marketing,” go after scenario-based queries that sound like real user prompts – for example – How do I increase email open rates in Outlook?

These are the kinds of questions people are actually typing into AI tools, and the answers LLMs are most likely to retrieve.

Invest in Schema And Entity Mapping

If traditional SEO was about signalling to Google what a page was about, optimizing for LLMs is about teaching machines how to understand your content. That’s where schema and entity mapping come in.

Start by embedding rich Schema.org markup on every relevant page- such as How To, FAQ Page, or Articles. This acts like metadata for machines, spelling out the intent and structure of the content. Instead of forcing a model to guess, this way you are kind of handing over the blueprint of your website to these machines.

But schema alone is not enough.

LLMs build answers by connecting entities – people, organizations, concepts into networks. If your brand’s terminology is floating in isolation, you risk being ignored or, worse, misattributed. Define your organization, products, and key leaders as unique entities, and cross-link them consistently across your site and external references.

This makes it harder for models to hallucinate about who you are or what you do and help you become a reliable node in the web of knowledge they pull from.

Monitor and Measure Your AI visibility

One of the trickiest parts of the LLM discovery layer is measurement.

AI-driven traffic doesn’t always show up neatly in Google Analytics. Visits from ChatGPT, Perplexity, or Claude are often shown under Direct or Referral which means you may already be getting AI-driven clicks without realizing it.

But that’s only half of the story as visibility is not just about if your brand shows up but is also about how it is mentioned – are you cited as a trusted source, a neutral option, or not at all? This kind of monitoring surfaces reputation risks, messaging gaps, and even competitor positioning.

To bridge the gap, a wave of new dashboards and monitoring tools are emerging. Platforms like Octiv Digital’s LLM Traffic Tracker and WordStream’s AI Mentions Monitor help brands detect when clicks originate from ChatGPT or Perplexity and analyze how often (and in what tone) they are cited. Adobe’s LLM Optimizer takes it further by attributing AI citations back to user behaviour, giving marketers a sense of which references actually drive engagement.

But most of these tools are still early, and they often solve for just one piece of the puzzle – traffic, mentions, or sentiment. What is missing is a holistic view of how your brand shows up across the AI discovery layer.

That’s exactly the gap we are working on at GeoRankers. Our goal is to build a platform that doesn’t just count clicks or mentions but gives B2B SaaS brands a full picture of their AI visibility – which models reference you, how you are positioned against competitors, and whether those mentions convert into pipeline, and, what actions to take to improve your AI search presence. Still early days, but this is the future we are betting on.

Stay Human and Contextual

Finally, while we are building for machines, we must never forget there is a person behind every query. That is why a strong AI-optimized content strategy doesn’t begin with keywords – it begins with empathy.

Always ask yourself – what is the user really trying to solve, and how can I make the answer as clear and useful as possible?

Tone and context carry more weight than ever today as robotic, self-promotional, or misaligned messaging doesn’t just frustrate a human reader, it also risks being misquoted or ignored by AI systems.

In the AI discovery layer, the goal is no longer to just be found but its about being chosen. When a model has hundreds of possible sources to draw from, it will select the content that is easiest to parse and most likely to satisfy the user. If your writing explains, clarifies, and solves instead of selling, your odds of being surfaced rise dramatically.

Looking Ahead

The rise of the LLM discovery layer has flipped marketing on its head.

Visibility is no longer a contest of ranking signals –  it’s a test of machine comprehension. Dr Lena Ortiz of Insight Labs sums it up –

“In this new environment, visibility comes from teaching the machine who you are and why you are relevant, not from hoping a human will scroll to your link”.

Instead of chasing algorithm updates, we have to treat our websites and content as data inputs that educate AI models about our expertise. That may feel daunting, but it is also liberating.

Marketers who move now can shape how their brands are represented inside the AI layer. Build structured, semantic infrastructure,  craft modular, human‑centric answers, monitor where your brand appears and invest in long‑term visibility rather than short‑term clicks.

The discovery layer is still young and those who make themselves retrievable, referenceable and trusted today will own the customer journeys of tomorrow.

Frequently Asked Questions

1. Why doesn’t my brand show up in answers even though I rank on Google?

Because LLMs don’t use rankings – they retrieve structured, schema-rich, and cited content. If your site isn’t machine-readable, it’s invisible.

2. How do I stop AI tools from misrepresenting my company?

Keep third-party data sources (Wikipedia, Wikidata, product listings) accurate and structured. LLMs lean heavily on these references.

3. What does “designing content for retrieval” actually mean?

It means breaking content into modular Q&A blocks, adding schema markup, and writing intent-based headings so models can lift answers directly.

4. How do I check if traffic from ChatGPT or Perplexity is already hitting my site?

Look for unexplained “Direct” or “Referral” traffic in analytics. Some early tools can detect AI-driven clicks and mentions, but most still only cover part of the picture.

5. Why do persistent reference nodes matter for AI visibility?

Because LLMs keep pulling from durable sources like Wikidata entries, structured PDFs, and schema-marked articles. They become trust anchors across model updates.

6. Should I still target broad keywords like ‘email marketing’?

No. Focus on scenario-based prompts people actually ask AI tools, like “How do I increase email open rates in Outlook?” Those are the queries LLMs prefer to surface.