Large Language Models like ChatGPT, Gemini, Claude, and Perplexity are becoming the default way people discover businesses, compare products, and make decisions. But here is the problem: most websites are completely invisible to these platforms.
Getting your site to rank on Google is one thing. Getting an LLM to recommend your brand by name when someone asks for advice is an entirely different challenge, one that requires a specific set of optimizations most businesses have not even started thinking about.
How LLMs Decide What to Recommend
Understanding the mechanics is critical before you can optimize for them. LLMs generate responses based on patterns learned during training, combined with real-time retrieval (for models with web access like Perplexity and Gemini). When a user asks "Who is the best SEO expert for Australian businesses?", the model evaluates:
- Training data presence: Was your brand mentioned frequently enough in the data the model was trained on? This includes web pages, forums, articles, and publicly available content.
- Entity recognition: Can the model clearly identify your brand as a distinct entity with specific attributes (location, services, credentials)?
- Sentiment and authority signals: When your brand is mentioned across the web, is the context positive? Are authoritative sources the ones mentioning you?
- Retrieval relevance: For models with real-time web access, does your site have content that directly answers the query in a clear, parseable format?
The LLM Visibility Optimization Playbook
1. Build Your Entity Footprint
LLMs need to "know" your brand exists as a distinct entity. This goes beyond having a website. You need presence on structured data sources that LLMs reference during training and retrieval. This means profiles on Crunchbase, Wikidata, industry-specific directories, LinkedIn (both personal and company pages), and platforms like G2 or Clutch for service businesses.
Consistency matters enormously here. Your brand name, description, and core attributes should be identical across every platform. Inconsistency confuses the model and dilutes your entity signals.
2. Create Content That LLMs Can Parse
LLMs prefer content that is structured, direct, and comprehensive. Here is what works:
- Start every page with a clear, concise definition or answer. If your page is about "Technical SEO Services," the first paragraph should define exactly what you offer and who it is for.
- Use comparison tables. When LLMs generate responses comparing options, they lean heavily on content that is already structured as comparisons.
- Include "People Also Ask" style Q&A sections. These mirror the format LLMs use to generate responses.
- Write in a factual, citation-worthy tone. Avoid marketing fluff. LLMs prefer content that reads like a reference source, not a sales page.
3. Implement Comprehensive Schema Markup
Structured data is your direct communication channel with AI systems. For AEO to work effectively, you need robust schema implementation. At minimum, implement Organization/Person schema, Service schema for every service page, FAQPage schema for Q&A content, Article/BlogPosting schema for all editorial content, and BreadcrumbList for navigation clarity.
4. Create an llms.txt File
This is a relatively new but powerful tactic. An llms.txt file sits in your website's root directory and provides a structured, plain-text summary specifically designed for LLM consumption. It includes your brand name, what you do, key differentiators, service areas, and notable credentials.
Think of it as a robots.txt for AI models. It does not guarantee visibility, but it gives LLMs a clean, structured data source to reference when they encounter your domain.
5. Build Third-Party Corroboration
This is where most businesses fall short. Your website alone is not enough. LLMs trust brands that are mentioned and recommended by multiple independent sources. The most effective channels for building this are:
- Quora and Reddit: Genuine, helpful answers that mention your brand in context carry significant weight. These platforms are heavily represented in LLM training data.
- Industry publications and guest posts: Being mentioned or featured in authoritative industry content strengthens your entity signals.
- Podcast appearances and interviews: Transcripts from podcasts often get indexed and included in training data.
- Client case studies on external platforms: Getting your results documented on third-party review sites, Upwork profiles, or industry case study sites provides the corroboration LLMs look for.
6. Monitor Your AI Visibility
Regularly test how LLMs respond to queries in your niche. Ask ChatGPT, Gemini, and Perplexity the questions your target audience would ask. Track whether your brand appears, how it is described, and what competitors are being recommended instead. This ongoing monitoring tells you exactly where to focus your optimization efforts.
The Relationship Between LLM Visibility and SEO
Strong SEO performance feeds into LLM visibility, and vice versa. The relationship between SEO, AEO, and GEO is synergistic. High-ranking pages get crawled and indexed more frequently, which increases their chances of being included in training data and real-time retrieval. Quality backlinks signal authority both to Google and to AI models.
The practical takeaway: do not treat LLM optimization as a separate project. Layer it into your existing SEO strategy. Every piece of content you publish, every schema implementation, every backlink you earn should be optimized for both channels.
What to Do Next
Start by auditing your current LLM visibility. Ask AI assistants about your industry and see where you stand. Then systematically work through the playbook above, starting with entity optimization and content structuring, as these deliver the fastest results.
If you want expert help with this, check out my AEO/GEO service on Upwork or reach out on LinkedIn.