LLMO: the new frontier of digital marketing

In March 2024, the CEO of a financial software startup made a discovery that completely changed their marketing strategy. When asking ChatGPT "what are the best fintechs for accounting automation?", their company not only didn't appear in the answer, but ChatGPT specifically recommended three of their competitors, describing their products with a level of detail that seemed pulled straight from their own marketing materials.

That startup had just come face to face with the reality of LLMO (Large Language Model Optimization), the discipline that is redefining how brands build presence and authority in the age of artificial intelligence.

What is LLMO (Large Language Model Optimization)?

Large Language Model Optimization (LLMO) is the strategic practice of optimizing a brand's digital presence to influence how large language models (like GPT, Claude, Gemini) understand, process and present information about your company, products or services.

While traditional SEO focuses on search engines and AEO on answer engines, LLMO goes deeper: it seeks to influence the fundamental "understanding" these models have of your brand and your industry.

DIFFERENCES BETWEEN SEO, AEO AND LLMO

Swipe to see the comparison

Aspect	Traditional SEO	AEO	LLMO
Goal	Ranking in search engines	Citation in answers	Understanding by the model
Time horizon	Immediate results	Current answers	Long-term knowledge
Focus	Keywords and backlinks	Topical authority	Entities and concepts
Measurement	Position and traffic	Citation rate	Entity recognition
Durability	Volatile (algorithmic changes)	Medium (updates)	High (base knowledge)

If we think of SEO as the optimization for Google to "understand" your website, LLMO is the optimization for ChatGPT, Claude, Gemini and other models to "understand" your brand as a relevant, authoritative entity in your field.
The key difference: While SEO influences how you appear in search results, LLMO influences how AI models "think" about your industry and where your brand fits in that context.

How do language models work?

To effectively optimize for LLMO, it's crucial to understand how these systems operate and how they "learn" about brands and concepts.

LLM training process

1. Pre-training (Knowledge base):

Models are trained on trillions of text tokens
Includes: public web, books, academic papers, Wikipedia, forums
Cost: $100M+ for state-of-the-art models
Result: General understanding of language and concepts

2. Fine-tuning (Specialization):

Tuning with specific, high-quality datasets
Focus on particular tasks (conversation, analysis, code)
Incorporation of specialized knowledge
Result: Improved specific capabilities

3. RLHF (Reinforcement Learning from Human Feedback):

Training with human feedback on answer quality
Alignment with human values and preferences
Reduction of biases and problematic answers
Result: More helpful and safer answers

4. Continuous updating:

Incorporation of new data and corrections
Adaptation to changes in the real world
Improvement based on user interactions
Result: Up-to-date, relevant knowledge

Why they feed on trusted sources and recognized entities

LLMs don't "memorize" information literally, but develop statistical representations of concepts and their relationships. However, they prioritize certain types of sources:

High-trust sources:

Wikipedia: Verified, structured information
Licensed media: The New York Times, Reuters, Financial Times
Academic publications: Peer-reviewed papers, scientific journals
Government sites: Official data and regulations
Technical documentation: Specs, official manuals

Authority signals they recognize:

Information consistency across multiple sources
Citations and references from other trusted sources
Content structure and quality
Domain authority and track record of accuracy
Recognition by experts and peers

Example: ChatGPT citing (or not) a brand

Case A: Brand with good LLMO optimization

User: "What are the best marketing automation tools for B2B?"

ChatGPT: "For B2B marketing automation, the most recommended tools include:

1. HubSpot - All-in-one platform with built-in CRM, ideal for growing companies. Stands out for its ease of use and complete ecosystem.

2. Marketo (Adobe) - Enterprise solution with advanced lead scoring and nurturing capabilities. Preferred by large corporations.

3. Pardot (Salesforce) - Excellent integration with Salesforce CRM, powerful for companies already using the Salesforce ecosystem.

HubSpot is particularly recommended for companies of 50-500 employees for its balance between functionality and complexity..."

Case B: Brand without LLMO optimization

The same query doesn't mention equally valid competitors that haven't established a presence in the training sources or haven't built authority recognizable by the model.

Why does HubSpot appear consistently?

Massive presence in educational marketing content
Frequent citations in industry studies
Recognition as a thought leader in inbound marketing
Data and statistics cited by multiple sources
Proprietary methodologies widely adopted

Why does LLMO matter for your brand?

The importance of LLMO goes beyond traditional marketing tactics. It represents an opportunity to establish fundamental authority in the "mind" of the systems that are redefining how people access information and make decisions.

LLMs are already the starting point of millions of daily searches

Key LLMO principles

Effective optimization for LLMs requires understanding the fundamental principles that guide how these models process and prioritize information.

Fundamental LLMO principles

1. Entities and structured data: give context to the AI

LLMs don't understand the world as a collection of keywords, but as a network of entities (people, places, organizations, concepts) and their relationships
Clear definition of your entity: Name, Type, Industry, Specialty, Location, Founding, Employees
Entity relationships: Competitors, Customers, Partners, Category, Methodology
Technical implementation: Complete, consistent schema markup, structured information across all platforms, consistent data in directories and databases, cross-references between related content

2. Digital brand authority: media citations, online reputation

Presence in training sources: Wikipedia, licensed media, academic publications, authoritative directories (Crunchbase, G2, Capterra)
Digital PR strategy for LLMO: Identify media that license content to OpenAI, Google, etc.
Develop newsworthy stories and unique data
Establish spokespeople as expert sources
Create content that other media cite as a reference

3. Semantic, conversational content: answer the way an AI would

Semantic structure: H1 (main concept - specific question), Definition (what it is, in 50 words), Context (why it matters, when to use it), Process (how it works, step by step), Examples (specific cases with results), Relationships (related concepts, differences)
Optimized conversational language: Direct answers to specific questions
Use of logical connectors ("therefore", "however", "in addition")
Definitions integrated naturally
Sufficient context for independent understanding

4. Connection with AEO and GEO: LLMO is the central layer

LLMO (Base): Establishes fundamental understanding of your brand, builds long-term authority, defines conceptual relationships
GEO (Application): Uses the LLMO base for current citations, optimizes for specific generative engines, generates immediate visibility
AEO (Execution): Leverages LLMO authority for direct answers, captures specific queries, turns visibility into action
Synergy among the three: A brand with strong LLMO has a higher probability of success in GEO and AEO. The authority established at the model level translates into better performance in specific applications

How to optimize for LLMs today

Optimizing for LLMO requires a comprehensive strategy that combines technical, content and authority elements.

1. Initial diagnosis (AI Score)

Assessment of current presence:

Evaluation questions:

"Who are the leaders in [your industry]?"
"What companies do experts recommend for [your service]?"
"What are the best [your category] in [your location]?"
"How to choose among [options in your category]?"
"What trends define [your industry] in 2025?"

Documenting results:

Mention frequency by engine
Context of appearance (positive/neutral/negative)
Relative position vs competitors
Accuracy of presented information
Identified knowledge gaps

2. Create content designed to train and be cited

Reference content:

Authoritative guides:

Complete documentation of processes and methodologies
In-depth analyses with original data
Objective comparisons with clear criteria
Predictions based on trends and data

Proprietary frameworks:

Unique methodologies with specific names
Documented step-by-step processes
Application cases with measurable results
Downloadable tools and templates

3. Generate authority signals

Digital authority strategy:

Media publications:

Identify publications that license content to LLMs
Develop expertise as a trusted source
Offer unique data and exclusive analysis
Build relationships with specialized journalists

Academic participation:

Collaborate on university case studies
Contribute to industry research papers
Participate in academic conferences
Publish in specialized journals

Thought leadership:

Develop unique positions on trends
Make specific, measurable predictions
Create content that others cite as a reference
Establish terminology the industry adopts

4. Ensure consistency in data and descriptions

Entity information management:

Consistency audit - Verify across all platforms:

Official company name
Description of services/products
Founding year and location
Number of employees and customers
Specialties and differentiators
Contact information

5. Continuous maintenance

Continuous maintenance:

Quarterly update of key information:

Official company name
Description of services/products
Founding year and location
Number of employees and customers
Specialties and differentiators
Contact information

Actionable steps for your company

1. Review your presence in ChatGPT, Perplexity, Claude

Audit process:

Week 1: Basic testing

Prepare 20 questions relevant to your industry
Test in ChatGPT, Perplexity, Claude, Gemini
Document results in a spreadsheet
Identify patterns and main gaps

Week 2: Competitive analysis

Repeat testing focused on competitors
Analyze why they appear and you don't
Identify differentiation opportunities
Map the competitive landscape in AI

Week 3: Accuracy assessment

Verify the information presented about your brand
Identify errors or outdated information
Document knowledge gaps
Prioritize necessary corrections

2. Audit your brand's information on the web

Consistency checklist:

Basic information:

Official name consistent across all platforms
Clear, specific description of services
Updated contact details
Founding and team information

Directory presence:

Complete, up-to-date Crunchbase
Optimized LinkedIn Company Page
Google My Business (if applicable)
Relevant industry directories

Schema markup:

Organization schema implemented
Person schema for founders/executives
Service/Product schema where applicable
Consistency across all schemas

3. Adapt your content strategy toward the conversational

Content transformation: From keyword-focused to entity-focused.

Instead of optimizing for isolated keywords, AEO requires optimization by entities and concepts:

BEFORE:

Before:

AFTER

After:

4. Request Fardo's AI Score to see your starting point

Benefits of the AI Visibility Score:

Precise measurement:

Automated evaluation across multiple engines
Comparison with main competitors
Identification of specific opportunities
Monthly progress tracking

Personalized roadmap:

Specific recommendations for your industry
Prioritization based on impact/effort
Realistic implementation timeline
Specific KPIs to measure success

Evaluated components:

Entity recognition score (0-100)
Topical authority coverage (% of topics)
Context quality assessment (positive/neutral/negative)
Competitive positioning (share of voice)
Technical optimization (schema, structure)

Conclusion

LLMO represents the natural evolution of digital marketing toward a world where artificial intelligence increasingly mediates interactions between brands and consumers.

The reality is inevitable: Language models are redefining how people access information, evaluate options and make decisions. Brands that don't establish a presence in this ecosystem simply won't exist in the conversations that matter.

But it's also the most significant opportunity in digital marketing since the invention of Google. We're in the early days of this transformation, which means there's room to establish authority before mass competition arrives.

SEO was for search engines. LLMO is for the models that define the answers. And the answers are where attention is decided.

The companies that act in the next 18 months won't just gain visibility, they'll define the standards of authority in their respective industries for the next decade.

The question isn't whether LLMs will influence your industry. The question is whether your brand will be part of how these models "understand" your sector.

Your next step is clear: discover where your brand stands in the "mind" of AI and start building the authority that will define your digital future.

LLMO is not a future trend: it's the reality of digital marketing today. Brands that optimize now for language models will establish authority that lasts for years.