LLM Optimisation: How to Make Your Brand Visible to Large Language Models

Millions of professionals now use large language models (LLMs) as a first port of call for research, learning, and decision-making. When a founder asks ChatGPT to recommend an AEO consultant, when a marketing manager asks Claude to explain the leading approaches to AI search, when a journalist asks Gemini to identify the authoritative sources in a niche — these queries are answered by systems that have formed views about who matters and who doesn’t.

LLM optimisation is the discipline of ensuring your brand lands on the right side of that assessment.

How LLMs Form Views About Brands and People

LLMs learn about brands, people, and concepts through two primary mechanisms:

1. Training data — The enormous corpus of web text, books, academic papers, and structured data used to train the model. If your brand was discussed in credible sources before the model’s training cutoff, it has likely formed a representation of you.
2. Real-time retrieval — Many modern LLMs (ChatGPT with Browse, Perplexity, Google Gemini) supplement their training data with real-time web retrieval. Fresh, crawlable, authoritative content can influence responses even for recently launched brands.

Understanding which mechanism applies to your situation is the starting point for LLM optimisation strategy.

Building Your Entity Graph for LLMs

LLMs organise knowledge in entity graphs — networks of related facts about people, organisations, and concepts. Your entity graph is the structured representation of your brand across the data that LLMs draw on. A well-constructed entity graph makes it easy for LLMs to retrieve accurate, positive information about you. A poorly constructed one leads to hallucinations, outdated information, or simple omission.

Person Entity (for individual practitioners)

For an individual expert or consultant, the person entity should clearly establish:

• Identity — Full name, professional title, organisation affiliation
• Credentials — Academic qualifications, certifications, years of relevant experience
• Knowledge domain — Specific areas of expertise (not just broad fields)
• Output — Books, articles, research, tools, or other work attributed to you
• Connections — Professional associations, co-authors, clients, collaborators
• Identifiers — URLs for LinkedIn, website, Twitter/X, ResearchGate, ORCID, or other persistent identifiers

Organisation Entity

For a brand or organisation, the entity should establish:

• Official name, founding date, location, and legal structure
• Products, services, and specific capabilities
• Key people (founders, executives) with their own person entities
• Client categories and geographic markets served
• Distinguishing positions or expertise (what makes you different)

Where to Publish Your Entity Data

LLMs draw entity data from specific source types more heavily than others. Prioritise these in order:

1. Your own website — Person schema, Organisation schema, and a comprehensive About/bio page are the most controllable signals. Implement JSON-LD schema.org markup for Person and Organization on your homepage and author bio pages.
2. LinkedIn — LinkedIn profiles are heavily indexed and frequently cited in LLM training data. A complete, keyword-rich LinkedIn profile with your credentials, experience, and expertise signals is essential.
3. Wikipedia — For brands or individuals of sufficient notability, a Wikipedia page is the single most powerful entity signal for LLMs. However, Wikipedia has strict notability guidelines — do not attempt to create a page unless you genuinely meet them.
4. Google Business Profile — For locally relevant businesses, a verified GBP provides structured entity data directly to Google’s systems.
5. Academic profiles — ResearchGate, ORCID, Academia.edu, and Google Scholar profiles signal academic credibility and are frequently present in LLM training data.
6. Press and media coverage — Being named, quoted, or profiled in credible publications creates the kind of independent third-party citations that LLMs treat as high-authority signals.
7. Industry directories and databases — Crunchbase (for startups and tech companies), professional association directories, and industry-specific databases contribute to entity completeness.

Auditing Your Knowledge Gap

A knowledge gap audit reveals what LLMs currently “know” about your brand — and the gaps or inaccuracies you need to address. The process:

1. Open ChatGPT, Claude, and Gemini in separate windows
2. Ask each: “Who is [your name / brand name]?” and “What does [your name / brand] specialise in?”
3. Document the responses: Are they accurate? Complete? Positive? Outdated? Does the model say “I don’t have information about…”?
4. Compare responses across models — inconsistencies reveal where your entity data is unclear or missing
5. For each gap or inaccuracy, identify the source type most likely to address it (schema, LinkedIn update, press coverage, etc.)

Content Strategy for LLM Visibility

Beyond entity data, the content you publish directly affects how LLMs represent your expertise. Content that performs well in LLM training and retrieval shares these characteristics:

• Definitional clarity — Your content defines terms clearly and precisely. LLMs use definitional content as source material for explaining concepts.
• Consistent positioning — Every piece of content reinforces the same expertise positioning. Breadth without depth confuses LLM representations of your knowledge domain.
• Attribution and citations — Citing your sources and being cited by others creates a web of attribution that LLMs can follow.
• Long-form depth — Comprehensive guides on narrow topics perform better in LLM citation than shallow coverage of broad topics.
• Regular publishing — Consistent publication signals that your site is an active, maintained resource — a positive signal for both traditional SEO and real-time retrieval systems.

Monitoring Your LLM Visibility

LLM visibility monitoring is still an emerging practice, but these approaches provide actionable data:

• Regular brand queries — Monthly queries across ChatGPT, Claude, Gemini, and Perplexity for your brand, name, and key topic areas. Document changes over time.
• Competitor comparison — Ask LLMs to compare you to competitors. How do they position you? What attributes do they highlight?
• Recommendation testing — Ask “Who are the leading experts in [your field]?” across multiple LLMs. Note whether you appear and how you are described.
• Citation tracking — Perplexity provides source citations. Search your key topics regularly and document how often your content is cited versus competitors.