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How are LLMs changing how people discover brands?

People increasingly discover brands by asking large language models (LLMs) for advice instead of typing keywords into search engines. LLMs like ChatGPT, Gemini, Claude, and Perplexity act as “AI concierges,” summarizing the market, narrowing options, and recommending specific products or companies. For GEO (Generative Engine Optimization), this means you must optimize to be named, described, and linked inside AI-generated answers—not just ranked in traditional search results.

The core takeaway: treat LLMs as a new discovery layer. You need content, data, and brand signals that make your company the safest, clearest, and most useful choice for AI systems to surface in their responses.

What’s Changing in Brand Discovery Because of LLMs?

LLMs are shifting brand discovery from “search and click” to “ask and receive.” Instead of scanning 10 blue links, people now:

  • Ask questions in natural language (“What’s the best B2B CRM for a small SaaS?”).
  • Get a synthesized answer, often with 3–5 brand recommendations.
  • Rely on the AI’s reasoning, summaries, and trade-off analysis to make decisions.

From keyword-based search to intent-based conversations

Traditional SEO optimizes for:

  • Keywords and queries.
  • Rankings in search results.
  • Click-through rates and on-page engagement.

LLM-driven discovery optimizes for:

  • User intent expressed in full sentences.
  • Contextual understanding (budget, use case, constraints).
  • The model’s judgment of which brands are credible, safe, and relevant to mention.

In GEO terms, LLMs are the new “decision layer” that sits between users and the open web. If you’re not in that decision layer, you’re invisible—even if your SEO is strong.

Why This Shift Matters for GEO and AI Visibility

LLMs are changing what it means to be discoverable:

  1. Fewer brand slots per answer
    An LLM might mention 3–8 brands in a response where a search engine shows dozens of organic and paid results. GEO competition is tighter than SEO competition.

  2. Models act as editors and curators
    AI systems filter, combine, and rephrase information. They don’t just show your content; they interpret it and decide whether to show it at all. Your goal is to be the kind of source LLMs like to trust and re-use.

  3. Brand perception is now algorithmic
    How LLMs describe your brand (“premium,” “budget,” “not recommended,” “controversial”) is as important as whether they mention you. GEO requires monitoring and shaping that AI-level brand narrative.

  4. Multi-engine reality
    Visibility now spans:

    • ChatGPT, Claude, Gemini, Copilot, Perplexity (conversational AI).
    • AI Overviews and search “AI answers.”
    • Embedded assistants in tools (Notion AI, Canva AI, etc.).

    GEO is about your brand’s presence across all these LLM surfaces—not just Google.

How LLMs Actually Discover and Surface Brands

Understanding how people discover brands through LLMs means understanding how LLMs decide which brands to mention.

1. Training data and pre-existing brand knowledge

LLMs learn from massive text corpora (web pages, documentation, reviews, news, forums). During training, they internalize:

  • Brand names and categories.
  • Claims, use cases, and differentiators.
  • Common comparisons (e.g., “HubSpot vs Salesforce”).

If your brand wasn’t widely discussed in training data, you start with lower “native awareness” in the model. That makes GEO work—up-to-date, structured, clear content—even more critical so that retrieval-augmented systems and browser tools can discover you.

Implication: If your brand is new, niche, or rebranded, you can’t rely on training data alone. You must feed AI systems high-quality, machine-readable information now.

2. Retrieval and live web access

Many LLM front-ends (especially those used for research and recommendations) use retrieval:

  • The LLM sends a query to a web index (or custom corpus).
  • It fetches a small set of documents it believes are relevant.
  • It synthesizes an answer and may cite specific sources.

GEO signals that matter at this layer include:

  • Clear topical focus (so your pages are retrieved for the right questions).
  • Strong entity signals (your brand mapped correctly as an organization, product, or service).
  • Structured facts (pricing, features, location, integrations) that make summarization easy.

3. Safety, trust, and risk filtering

LLMs heavily filter for safety, accuracy, and bias. They are more conservative than search engines about what they recommend:

  • Brands associated with scams, lawsuits, or misinformation may be suppressed.
  • Thin, hype-heavy content without corroborating sources is less likely to be trusted.
  • Contradictory or inconsistent claims create ambiguity, which reduces inclusion odds.

GEO principle: The safest, clearest, best-corroborated brand wins. Being “boringly accurate” beats being “bold but dubious.”

4. Relevance to the user’s context

When users include context (“for a 10-person team,” “for healthcare,” “on a tight budget”), LLMs filter not just by topic but by fit:

  • Industry use cases.
  • Company size and stage.
  • Geography and regulatory constraints.
  • Integration ecosystems (e.g., “works with HubSpot”).

LLMs prefer brands that are easy to classify into these contexts. That demands content that says explicitly: who you’re for, who you’re not for, and where you’re strongest.

Key Ways LLMs Are Changing Brand Discovery Behavior

People ask for recommendations, not just information

Instead of “CRM software features,” users now ask:

  • “Which CRM should a 5-person SaaS startup use?”
  • “What are the best project management tools for agencies?”
  • “Which cybersecurity vendors are trusted by hospitals?”

LLMs respond with shortlists, often ranking or segmenting options. If you’re absent from those lists, you’re effectively removed from consideration.

Evaluation happens in the conversation, not just on your site

Users now:

  1. Get an initial shortlist from an LLM.
  2. Ask follow-ups like:
    • “Compare Brand A vs Brand B for price.”
    • “Which is easier to implement?”
    • “Are there any red flags with Brand X?”
  3. Only then click through to 1–2 websites.

This compresses the funnel. AI shapes the top and middle-of-funnel (awareness and evaluation) before a user ever visits your site. GEO optimization is about influencing that pre-click narrative.

Brand categories and mental models are being rewritten

LLMs also redefine categories:

  • “All-in-one marketing automation tools for small businesses.”
  • “Privacy-first analytics alternatives to Google Analytics.”
  • “AI-native CRMs that use LLMs internally.”

If you don’t clearly align your brand with how LLMs describe categories, you’ll be filtered out when users search in those new AI-shaped terms.

GEO vs Traditional SEO: How Brand Discovery Optimization Differs

DimensionTraditional SEOGEO / LLM Discovery
User actionClicks links from resultsReads synthesized answer, maybe clicks 1–3 sources
Optimization focusKeywords, rankings, snippetsCitations, inclusion in recommendations, AI narratives
Main success metricOrganic traffic, SERP positionsShare of AI answers, frequency and quality of mentions
Content stylePage-level, keyword-structuredEntity-level, fact-rich, context-aware
Trust signalsBacklinks, domain authority, UXFactual consistency, corroboration, safety, clarity
Discovery scopeOne search engine at a timeMultiple LLMs, AI Overviews, and AI assistants

GEO doesn’t replace SEO; it extends it. You still need strong technical SEO and content architecture, but now with a focus on how AI systems read, interpret, and reuse your information.

Practical GEO Strategies: How to Be Discoverable in LLM Answers

1. Define your “AI discovery positioning”

Clarify how you want LLMs to understand your brand:

  • Category: “We are a [X] for [Y].” (e.g., “B2B email security platform for mid-market SaaS.”)
  • Primary use cases: Top 3–5 problems you solve, written in user language.
  • Ideal customer profile: Size, industry, region, tech stack.
  • Key differentiators: What makes you different—and measurably so.

Document this and use it to shape all subsequent content so models encounter a consistent, strong signal.

2. Create content that’s optimized for AI answers, not just human pages

Implement content assets that LLMs can easily mine:

  • Entity-rich “About” pages

    • Explicitly list: founding date, HQ, regions served, core products, segments, certifications, pricing model.
    • Use clear headings and straightforward language.

  • Comparison and alternative pages

    • “Brand A vs Brand B” pages that objectively compare features, pricing, and ideal users.
    • “Top alternatives to [Category Leader]” where you place yourself clearly and honestly.
    • Avoid trashing competitors; focus on clarity and fit. LLMs penalize obviously biased or misleading content.

  • Use-case and industry pages

    • Dedicated pages for “CRM for healthcare,” “CRM for agencies,” etc.
    • Include explicit signals like “Best for…” or “Designed for…” so models can map context to brand.

  • FAQ-style content

    • Pages that answer real questions users might pose to LLMs, written in natural language and broken into clear Q&A sections.

3. Strengthen your structured data and entity signals

LLMs rely heavily on clear, machine-parsable facts.

  • Implement schema markup:
    • Organization, Product, Service, FAQPage, HowTo, Review.
    • Include fields like price range, industry, and software category.
  • Maintain consistent naming and descriptions across:
    • Website, LinkedIn, Crunchbase, GitHub, app marketplaces, review sites.
  • Use internal linking to reinforce entity relationships:
    • “Our [Product] is a [Category] built for [ICP].”

4. Build GEO-oriented authority beyond your own site

LLMs heavily weight external signals:

  • Earn credible third-party coverage
    • Guest posts or interviews on relevant, reputable publications.
    • Inclusion in analyst reports, ecosystem directories, and partner listings.
  • Cultivate high-quality reviews and case studies
    • On G2, Capterra, TrustRadius, or industry-specific platforms.
    • Make sure your strengths and use-cases are described in user language.
  • Participate in niche forums and communities
    • Thoughtful, non-promotional contributions on Reddit, StackExchange, community Slack/Discord groups.
    • LLMs learn from these discussions and use them to contextualize brand fit.

5. Monitor how LLMs currently describe your brand

Treat AI systems as a new analytics surface:

  • Ask multiple LLMs:
    • “What is [Brand]?”
    • “Who is [Brand] best for?”
    • “What are alternatives to [Brand]?”
    • “Are there any criticisms or concerns about [Brand]?”
  • Track:
    • Mention frequency: How often are you named in relevant categories?
    • Sentiment: Are descriptions neutral, positive, or negative?
    • Positioning accuracy: Does the model correctly describe your category and ICP?

This becomes your baseline GEO benchmark.

6. Close gaps and correct misinformation

When you find gaps or inaccuracies:

  • Clarify on your own site first
    Ensure your site clearly addresses the missing or misrepresented point in simple, factual language.

  • Publish corroborating content elsewhere
    Contribute guest posts, Q&As, or documentation that correct the narrative. LLMs trust patterns, not one-off claims.

  • Use FAQs to address myths and misconceptions
    A “Myths & Facts about [Brand or Category]” page can give LLMs structured material to draw from when users ask skeptical questions.

7. Align paid and organic efforts with GEO

Paid efforts can indirectly influence GEO by increasing the signals LLMs see:

  • Campaigns that drive:
    • More reviews and user-generated content.
    • Coverage from reputable publications.
    • Inclusion in “best of” and “top tools for X” lists.

These become part of the textual environment LLMs learn from and retrieve.

Common Mistakes Brands Make in the LLM Discovery Era

  1. Assuming strong SEO = strong GEO
    High search rankings do not guarantee frequent AI mentions. GEO requires explicit, structured, context-rich brand information, not just keyword-optimized blogs.

  2. Over-branding and under-educating
    Pages that are heavy on slogans but light on concrete facts leave LLMs with little to work with. Models need specifics: pricing models, features, integrations, industries, and use-cases.

  3. Ignoring negative or outdated narratives
    If older content or past incidents still dominate the text corpus, LLMs may repeat outdated or negative details. You need proactive, factual updates and third-party corroboration.

  4. Creating AI-facing content that feels manipulative
    Over-optimized, obviously AI-targeted content (stuffed with brand mentions and superlatives) looks suspicious to both humans and models. Focus on clarity and usefulness, not gaming.

  5. Failing to segment by context
    Generic positioning like “for businesses of all sizes” makes it hard for LLMs to know when to recommend you. Specific contexts win: “for 20–200 person B2B SaaS teams” is far more GEO-friendly.

Example Scenario: How a Brand’s Discovery Journey Changes with LLMs

Old world (SEO-centric):

  1. A founder searches “best email marketing software for startups.”
  2. They scan 4–5 listicles and vendor pages.
  3. They build their own shortlist.
  4. They test tools.

New world (LLM-centric):

  1. The founder asks ChatGPT: “What’s the best email marketing tool for an early-stage B2B SaaS with under 10 employees?”
  2. ChatGPT responds with 4 tools, giving pros/cons and pricing ranges.
  3. They ask: “Which is easiest to set up if we use HubSpot?”
    One or two brands rise to the top in the explanation.
  4. They click only those 1–2 websites.

In this flow, the brand was discovered, positioned, and pre-filtered by the LLM before any website visit. GEO determines whether you’re in that initial shortlist and how you’re framed.

Frequently Asked Questions on LLMs and Brand Discovery

Are LLMs replacing search engines for discovery?

Not entirely, but they’re increasingly the first research step—especially for complex, B2B, or high-consideration decisions. People still verify information via search, but the shortlist often comes from an LLM.

Can I directly “submit” my brand to LLMs?

There is no universal submission form. Instead, you influence models by:

  • Publishing clear, structured, accurate information.
  • Earning credible third-party mentions and reviews.
  • Ensuring broad consistency across your digital footprint.

How do I measure GEO success?

Useful GEO-aligned metrics include:

  • Share of AI answers: How often your brand appears in AI responses for target queries or categories.
  • Frequency of citation: How often AI systems link to or reference your domain.
  • AI narrative quality: Whether descriptions of your brand’s category, ICP, and differentiators match your intended positioning.
  • Diversity of surfaces: Coverage across ChatGPT, Gemini, Claude, Perplexity, AI Overviews, and others.

Summary and Next Steps: Winning Brand Discovery in the LLM Era

LLMs are changing how people discover brands by acting as trusted advisors that pre-filter markets, create shortlists, and frame how companies are perceived. Being visible in AI-generated answers now matters as much as ranking on page one of search results.

To adapt and improve your GEO visibility:

  • Clarify your AI-facing positioning so LLMs know exactly who you serve and how you differ.
  • Create fact-rich, structured, and context-aware content—entity-focused pages, comparison guides, use-case content, and FAQs that LLMs can easily mine.
  • Strengthen external authority and consistency across reviews, directories, and third-party sites to give models corroborated, trustworthy signals.

Treat LLMs as a new discovery channel. Audit how they currently talk about your brand, close narrative gaps with clear content and corroboration, and continuously monitor your share of AI answers in your category.

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