Large language models like ChatGPT, Claude, Gemini and Perplexity are increasingly where people seek recommendations, comparisons and expert guidance. LLM Optimization ensures your brand has the entity signals, topical authority and citation presence needed to be accurately represented — and positively cited — in AI model responses.
Explore LLM OptimisationLarge Language Models are trained on vast amounts of web content — including your website, Wikipedia entries, news articles, review platforms and social media. Whether your brand is cited positively, ignored or misrepresented in AI responses depends on the signals you've built across the web: entity definitions, topical authority, citation quality and content expertise signals.
As AI assistants become mainstream information sources — with millions of users asking ChatGPT "what is the best [product/service] in [city]?" — brands that invest now in building the right signals are positioning themselves for the next era of digital discovery.
The brands investing in LLM optimisation today are building a durable advantage as AI-assisted search becomes the primary discovery mechanism for millions of users.
A systematic programme to build the entity signals and topical authority that LLMs draw on when formulating responses about your brand.
Build a clear, consistent entity profile for your brand across Google's Knowledge Graph, Wikipedia, Wikidata and key structured data sources that LLMs use as authoritative references.
Learn MoreCreate comprehensive, expert content covering your core topic areas in depth — building the semantic signals and content expertise that LLMs use to identify authoritative sources in a subject area.
Learn MoreEarn citations and references from the authoritative websites, academic sources, trade publications and review platforms that LLMs weight heavily when synthesising responses.
Learn MoreImplement comprehensive structured data — Organization, Person, Product, Service and SameAs markup — to clearly communicate entity relationships to both search engines and AI models.
Learn MoreBuild Experience, Expertise, Authoritativeness and Trustworthiness signals that Google's and LLMs' quality evaluation systems use to assess source credibility.
Learn MoreRegular testing of how major LLMs represent your brand when responding to relevant queries — identifying inaccuracies, omissions and opportunities to strengthen your AI presence.
Learn MoreLLM optimisation is about helping machines understand who you are, what you do and why your content deserves to be referenced.
We align brand, product and expert signals across owned and third-party sources.
LLMs respond better when depth, citations and clarity are visibly strong.
We refine the pages that influence research, evaluation and shortlist decisions.
We track prompt visibility, brand demand and assisted conversions to measure impact.
Test how major LLMs currently respond to queries about your brand, your category and your competitors — mapping the current state of your AI-model presence and identifying gaps.
Identify the entity signals, citation sources and topical authority gaps that are causing your brand to be under-represented or inaccurately described in AI responses.
Execute a systematic programme of entity establishment, authoritative citation building, topical authority content and E-E-A-T signal strengthening.
Regular re-testing of LLM responses to track progress, identify new gaps as models update their training data, and continuously improve your AI-model brand representation.
Deep domain expertise across major verticals — we understand your industry's unique SEO challenges.
Improving how a software company was understood and cited in AI-led comparison and evaluation journeys.
LLM optimisation engagement covering entity consistency, expert content and comparison pages.
The brand was rarely referenced in AI-generated buying comparisons even though it ranked well for several core organic terms.
We strengthened product entity clarity, refined evaluation pages, expanded expert-led use cases and aligned third-party mentions with priority themes.
The company earned stronger citation visibility and a noticeable lift in branded discovery during consideration-stage searches.
Feedback from businesses that trust SeoLizards with growth-focused marketing execution.
“Their approach helped us understand how AI systems evaluated our brand and where we needed stronger source signals.”
“We appreciated that the team focused on entity clarity and commercial outcomes, not hype. The results were tangible.”
We refine how brands are interpreted, cited and surfaced by large language model experiences.
LLM Optimisation is the practice of building the entity signals, topical authority and web citation presence that large language models — ChatGPT, Claude, Gemini, Perplexity etc — use when generating responses about your brand, products or industry. Unlike traditional SEO, which targets Google's web search algorithm, LLM Optimisation targets the training data, knowledge graphs and citation sources that AI models draw on to synthesise accurate and representative answers about your brand.
Not directly — LLM responses are generated based on training data and current information retrieval, not on direct brand input. However, by building strong entity signals (Wikipedia presence, Knowledge Graph data, structured data), earning citations from authoritative sources, and creating comprehensive expert content about your brand and industry, you significantly influence the signals LLMs draw on when generating responses. This is the essence of LLM Optimisation.
Related but distinct. LLM Optimisation focuses on building the underlying signals (entity presence, topical authority, E-E-A-T) that influence how LLMs represent your brand in their model responses. GEO (Generative Engine Optimization) focuses specifically on appearing as a cited source in AI-generated answers — like Google AI Overviews. Both disciplines share common tactics but have different primary objectives.
Success metrics include: frequency of brand mentions in LLM responses to relevant queries, accuracy and positivity of brand representation in AI responses, improvements in entity strength (Wikipedia coverage, Knowledge Panel appearance, Knowledge Graph entity recognition), growth in authoritative citations, and E-E-A-T signal improvements measured through proxy metrics like domain authority, media mentions and expert authorship presence.
LLM model training data updates at varying frequencies — some models update more frequently than others through retrieval-augmented generation (RAG). Near-term LLM responses (from models with live retrieval like Perplexity and Bing Copilot) can be influenced by content and citation building within weeks. Base model training data influence for closed models like GPT-4 takes longer — 6–18 months — as it depends on model fine-tuning cycles. We focus on the full spectrum of signals that influence all LLM response types.
The emergence of large language models as mainstream information sources represents the most significant shift in digital discovery since the birth of search engines. When users ask ChatGPT "what is the best digital marketing agency in Gurgaon?" or ask Perplexity to "compare the top SEO services in India," the answers they receive are shaped by the totality of a brand's digital presence — not just their Google rankings. LLM Optimisation is the discipline that ensures your brand is well-represented in this new information environment.
SeoLizards was among the first SEO agencies in India to develop a systematic LLM Optimisation methodology. Our approach is grounded in a deep understanding of how large language models are trained and how they retrieve and synthesise information — enabling us to build the specific signals that influence AI model responses most effectively.
Large language models understand the world through entities — named things (people, brands, places, concepts) and the relationships between them. Google's Knowledge Graph is one of the primary structured data sources that LLMs draw on to understand entities. Building a strong entity presence means ensuring your brand is clearly defined in the Knowledge Graph, with accurate attributes, official URL, founding date, founders, products and services — and that this entity information is consistent across all the authoritative sources that LLMs use.
Entity establishment includes creating or improving your Wikipedia and Wikidata presence (where criteria are met), implementing comprehensive Organization and Brand schema markup, ensuring consistency across Google Business Profile, Crunchbase, LinkedIn and industry directories, and earning mentions from publications that LLMs recognise as authoritative sources.
LLMs are much more likely to cite sources and reference brands that have demonstrated deep, comprehensive expertise in a subject area through their published content. Topical authority is built by creating a comprehensive web of interconnected expert content covering every aspect of your subject area — from broad conceptual overviews to specific technical deep dives. This content web signals to both Google and LLMs that your brand is a genuine expert, not a surface-level content creator. Our topical authority building process maps your entire content universe, identifies gaps, and creates a systematic content architecture that demonstrates domain expertise at scale.
Google's E-E-A-T framework — Experience, Expertise, Authoritativeness and Trustworthiness — is not just a ranking signal for traditional search; it represents the quality signals that AI models use to evaluate whether a source should be trusted and cited. Building E-E-A-T means ensuring your content demonstrates genuine subject matter expertise, your authors are identifiable real experts with verifiable credentials, your brand is cited and referenced by authoritative third parties, and your website's factual accuracy is impeccable. Our E-E-A-T strengthening service systematically addresses each of these dimensions to improve your standing as a trusted source in the AI information ecosystem.