LLMO Playbook: Making Your Brand the Answer AI Gives

Large Language Models are now the first stop for millions of questions. They’re not searching Google. They’re asking Claude. They’re asking ChatGPT. And when they get an answer that mentions your product, your brand, or your solution-that’s not a lucky accident. It’s a system you built.

This is LLMO: Large Language Model Optimisation. It’s the natural evolution of SEO, but it plays by different rules. The old playbook-keywords, backlinks, domain authority-still matters for Google. But getting picked by an LLM requires a different architecture entirely.

Why LLMs Are Already Changing Where Your Customers Find You

About 30-40% of search queries now end in an LLM or AI chat interface rather than traditional search results. That number’s moving higher. And here’s the thing: when someone gets an answer from Claude about “best CRM for SaaS founders in Australia,” they’re not seeing your Google ranking. They’re seeing what’s in Claude’s training data and what it retrieves in real time.

You have two surfaces to own:

• Training data – content published before the model’s knowledge cutoff, which shaped how the model thinks about your space
• Retrieval augmented generation (RAG) – real-time content that gets pulled in when answering a query, giving you a second chance to be the answer

Both require intent. Neither happens by accident.

Building Your Brand Into LLM Training Data

The training data layer is already locked for most major models. OpenAI’s training data for GPT-4 has a knowledge cutoff. Claude’s does too. You can’t change that. But you can shape what future models learn about you, and you can influence what gets fed into RAG systems right now.

The strategy has three parts:

1. Publish specific, structured answers to questions your customers actually ask. Not blog posts about “the future of X.” Answer: “How do I integrate Stripe with my subscription platform in 15 minutes?” “What’s the cheapest way to run LLM inference in Australia on your own hardware?” “Why did we choose PostgreSQL over MongoDB for this product?”
2. Make it crawlable and indexable. LLMs and RAG systems pull from public web content, documentation, and indexed pages. If it’s behind a login or a paywall, it won’t be included. Open it up strategically.
3. Build authority through specificity. A 8,000-word guide comparing five CRM platforms gets picked up. A 500-word intro to CRM doesn’t. Depth + specificity = what RAG systems will surface.

A fintech we worked with realised their product was being described incorrectly in LLM responses-positioned as a competitor to solutions it wasn’t competing with. They published a single technical deep-dive explaining exactly what problem they solved and why existing comparisons missed it. Within two months, LLM responses shifted. That’s the power of specific, well-structured content.

Claiming the Real-Time Retrieval Layer

RAG (retrieval augmented generation) is where you get real leverage right now. When someone asks an LLM a question, the system can pull in fresh data-usually from your documentation, your website, or APIs you’ve exposed. This is your second window to be the answer.

Most businesses aren’t thinking about this at all. Here’s how to own it:

Make your documentation queryable. If you have a SaaS product, your API docs, feature guides, and FAQs should be structured and publicly available. Use proper heading hierarchy, short sections, and clear language. An LLM retrieval system will pull your exact wording into the response. You get a direct quote in front of the user.

Expose your product through structured data. If you’re an e-commerce or marketplace business, structured data (schema markup) tells LLMs and their retrieval systems exactly what you offer: pricing, availability, specifications. A user asking “What’s the cheapest B2B accounting software in Australia?” will get your product with pricing if you’ve marked it up. Most competitors won’t have.

Build an API for integrations. This is longer-term, but LLMs increasingly call APIs when answering questions. If you’re a property valuation platform, a mortgage broker’s LLM agent might call your API to fetch real-time estimates. If you’re a payroll software, an accountant’s AI agent might pull employee data from you to answer tax questions. Being callable makes you part of the answer.

Be listed in AI tool directories and model provider ecosystems. OpenAI’s plugin system and Anthropic’s Resources feature let companies surface their products to LLM users. These slots still exist and are far less crowded than Google Ads. Get listed.

SEO and LLMO Are the Same Strategy Now

Here’s a hard truth: optimising for Google and optimising for LLMs require almost identical content architecture.

Both reward:

• Clear, specific answers to real questions
• Good information architecture and heading structure
• Topic depth and topical authority
• Fresh, regularly updated content
• Links and references to trusted sources (which builds trust for both systems)
• Mobile accessibility and page speed

The difference is in intention and measurement. Google’s looking at clicks and engagement time. LLMs are looking at whether you provide the definitive answer. Google rewards a clickthrough to your page. LLMs reward being cited directly in their response.

If you’re talk to Amora about your build, we’ll audit your content strategy against both. Most businesses are doing one or the other well. The ones winning are doing both.

Practical Next Steps

You don’t need to rebuild everything tomorrow. Start here:

1. Audit your documentation. Does it answer the specific questions your customers ask LLMs? (You can check this by asking the LLMs yourself.)
2. Identify high-intent queries. What are the questions that, if answered with your product as the solution, would convert? Write definitive guides on those.
3. Structure your content properly. Headings, short paragraphs, lists, and clear CTAs. Make it easy for both humans and LLMs to extract the answer.
4. Publish the output. Make it publicly crawlable. Push it to your site, documentation, and blog. Ping search engines and RAG systems.
5. Add schema markup. Use JSON-LD to label what you’re offering. Price, availability, use cases. Be explicit.
6. Monitor and iterate. Ask LLMs your customer questions monthly. See if you’re being mentioned. If not, adjust your content and republish.

LLMO isn’t a separate channel or a new hire. It’s how you think about owning the conversation when the first interface your customer uses isn’t Google-it’s an AI.

The founders who move fastest here will own the next wave of customer acquisition. The ones who wait will be competing for scraps in Google’s SERP.