Practical Framework for Brand Visibility in LLMs

Every brand today faces a new question: How do we actually show up inside LLMs?

These systems are not just answering our queries. They are forming a public understanding of our brands, repeating what they learn not only back to us but to everyone else. And what they learn comes from everything in our content ecosystem: our site, socials, campaigns, press coverage, reviews, and even the questions consumers ask about us.

The first step is noticing the gap between what we want to be known for and what the models currently surface. Most brands find a mismatch. What they intend to represent does not always match what LLMs reflect back. That gap often comes from the second layer: the broader content ecosystem that feeds the models, including third-party voices and user-generated content.

The next layer is understanding consumer intent. People turn to LLMs with a blend of functional needs and emotional context. They might be asking for instructions, but they might also be navigating a life moment. If we do not map those drivers, we miss why they are searching in the first place.

Once you see how you appear and why users search the way they do, the opportunities and risks become clear. And because the models evolve constantly, visibility requires continuous monitoring and stewardship.

Here is our Practical Framework for Brand Visibility in LLMs

Phase 0: Benchmark

Before shaping how you appear in LLMs, you must know how you show up everywhere else. This phase establishes the foundation that reveals your real visibility and influence across channels.

• Identify total share of voice across search, social, articles, video, and marketing
• Measure category visibility and competitor presence
• Document your current reach and influence trends
• Establish a baseline to understand the potential impact of AI visibility

Notes: This is where you quantify your current public footprint. Include competitive benchmarking and total share of voice. Without this baseline, you cannot understand how much LLM visibility can help or where improvement is feasible.

Phase 1: Alignment

Alignment clarifies the purpose behind your visibility efforts. You must know what you want to be known for before asking how LLMs should represent you.

• Define what you want your brand to be known for
• Identify whether your goal is reach, leads, authority, or emotional connection
• Build a simple goal tree that clarifies priorities
• Align stakeholders around the intended brand perception

Notes: Define what you want to represent as a brand, then later compare it to how LLMs portray you. Your desired perception must be explicit before any assessment.

Phase 2: Brand Assessment

This phase gives you a clear view of how you actually appear inside LLMs. You are teaching the model who you are, so you must first understand how it currently interprets you.

• Query multiple LLMs for your brand profile
• Identify what is accurate, inaccurate, or missing
• Compare desired perception with actual LLM output
• Capture themes, associations, and surprising interpretations

Notes: Use specialized tools to audit your LLM presence. Evaluate whether what appears reflects your intended positioning or something completely different. Include competitive benchmarking because LLMs rank and relate brands implicitly.

Phase 3: Consumer Analysis

Consumers using LLMs may have different questions, motivations, and emotional needs compared to traditional channels. Understanding their context ensures you match your content to their reality.

• Study the questions users ask LLMs about your category
• Identify functional needs and emotional drivers
• Map life moments and emotional contexts shaping queries
• Track how LLM channel behavior differs from search or social

Notes: Map the emotional and contextual triggers behind queries. Understand who is looking, what they are looking for, and why. LLM users may have hybrid or evolving needs that differ from traditional funnels.

Phase 4: Gap and Opportunity Analysis

You now compare what you want to be known for with what LLMs actually show. This reveals risks, misinformation, and the open opportunity field available to you.

• Compare your intended brand story with current LLM representation
• Identify missing content, outdated signals, and inaccuracies
• Assess risks across perception, supply chain concerns, and credibility
• Surface opportunities to clarify, differentiate, or reposition

Notes: Look for negative perceptions, missing advocates, or harmful associations. Identify root vulnerabilities and uncover strategic openings that your competitors have not claimed.

Phase 5: Value Strategy

Value strategy defines the core value your brand creates and connects it to the needs identified earlier. This becomes the bridge between insights and your public narrative.

• Clarify the value your brand delivers to consumers
• Tie value to consumer needs, questions, and moments
• Define the value of your full product ecosystem
• Translate value insights into a simple, repeatable narrative

Notes: The value strategy becomes the anchor for all later content. It synthesizes consumer realities, market gaps, and your desired perception into a value story that LLMs can learn and repeat.

Phase 6: Content Strategy

This is where your brand expresses its value in ways LLMs can understand and surface. Content is the vehicle that converts value into visibility.

• Build narrative content that makes your value clear
• Map content to functional and emotional use cases
• Create assets structured for LLM comprehension
• Maintain consistent clarity and authority across channels

Notes: LLMs learn from your website, socials, press, reviews, campaigns, and third-party content. Content must be consistent and easily understood by the model.

Phase 7: Technical Content Strategy

Technical structure determines whether your content is machine readable and retrievable. This is the infrastructure that supports discoverability inside models.

• Apply schema to help AI systems interpret content
• Strengthen internal linking to signal hierarchy
• Use structured formats optimized for model ingestion
• Leverage tools like the ChatGPT API for broader distribution

Notes: This is the engine room of discoverability. Strong metadata, clear hierarchy, and clean structure are what allow LLMs to reliably surface your brand.

Phase 8: Monitoring Loop

Models evolve, consumer behavior shifts, and competitor content changes. Monitoring ensures your visibility keeps pace with a dynamic AI environment.

• Re query LLMs regularly to identify changes in representation
• Track new consumer questions and emerging needs
• Monitor competitor movement inside LLM ecosystems
• Adjust your narrative, content, and structure based on new signals

Notes: LLM visibility is never static. The monitoring loop ensures your brand stays current, relevant, and aligned with evolving model behavior and user expectations.

Ryan Edwards, CAMINO5 | Co-Founder

Ryan Edwards is the Co-Founder and Head of Strategy at CAMINO5, a consultancy focused on digital strategy and consumer journey design. With over 25 years of experience across brand, tech, and marketing innovation, he’s led initiatives for Fortune 500s including Oracle, NBC Universal, Sony, Disney, and Kaiser Permanente.

Ryan’s work spans brand repositioning, AI-integrated workflows, and full-funnel strategy. He helps companies cut through complexity, regain clarity, and build for what’s next.

Connect on LinkedIn: ryanedwards2