THE STARTING POINT
Large language model optimisation (LLMO) is used as a label for improving how AI-assisted systems discover and describe a business. A useful starting point is a consistent identity, clear service information and traceable evidence. You cannot directly rewrite a third-party model’s memory or guarantee a recommendation.
Give the business one clear identity
Use the genuine trading name and a consistent explanation of what the company does. Make the official domain, contact route, country served and delivery model easy to find. If the business has similarly named competitors, add factual context such as the products or services offered rather than repeating vague claims about being “the leading company”.
Review the about page, service pages and profiles you control for contradictions. Old telephone numbers, incompatible price statements and different descriptions make life harder for customers too. Maintain a facts sheet so the next website update, directory entry or sales document uses the same approved information.
Separate an offer, a demonstration and evidence
A service page describes what you can deliver. A portfolio record shows work you actually completed. A concept demonstrates an approach you could build. Label them correctly, then explain the relevant details: scope, responsibilities, integrations, limitations and ownership. Do not present stock photographs as the team or concept dashboards as real client results.
Use original screenshots, project links or attributed feedback with permission. An example calculation should expose its assumptions. A comparison should state when a recommendation changes. This gives a reader a way to check the information instead of relying on a confident tone.
Keep machine-readable information tied to visible facts
Use appropriate organisation, service, article and breadcrumb data when it accurately describes the page. Check that business details in the markup match what a visitor can read. Structured data is one description of your content; it is not a hidden space for extra claims, fabricated ratings or promises about AI recommendations.
Google explicitly says that special AI text files, including llms.txt, are not required for its search visibility and are ignored for that purpose. Other systems may have their own documented support. Implement a file or protocol only when you have a specific, verifiable use case, and maintain the ordinary website first.
Source: Google: optimising for generative AI features. Checked 13 October 2026.
Test accuracy with a repeatable question set
Write a few realistic questions such as “Does this business serve South Africa remotely?” and “What information is needed for a custom CRM quote?” Record the system, date, context and cited sources. Check whether the answer is factually correct. Separate wrong source information on your own website from unsupported claims generated by the external system.
Fix information you control, then monitor again. Keep a record of changes so you can assess what improved. A single prompt response cannot establish market visibility: answers can differ between sessions and systems. Use the observation log alongside actual customer questions and enquiry attribution, and avoid claims that a business has a permanent position inside a model.
PUT IT INTO PRACTICE
Your first-release checklist
- Maintain an approved business identity and service facts sheet.
- Correct contradictions in the site and profiles you control.
- Label real work, demonstrations and illustrative figures accurately.
- Match structured data to visible content without unsupported ratings.
- Track answer accuracy with a dated question and citation log.
What to measure
Start with your own baseline. These are definitions for your team to track, rather than promised improvements or industry benchmarks.
- Fact consistency
- Review whether names, contact routes, scope and service areas agree across maintained pages.
- Observed answer accuracy
- Record correct and incorrect descriptions in a repeatable sample, with source links.
- Evidence coverage
- Check whether meaningful claims have a verifiable explanation or supporting record.
Useful questions
How is LLMO different from GEO?
Terminology overlaps. This guide focuses on identity, factual consistency and answer accuracy; the GEO guide focuses on discovery through generated search answers. They share normal SEO and useful content foundations.
Can we insert our business into an AI model?
Publishing a page does not directly control a third-party model’s training, retrieval or answers. Improve the sources you control and use documented platform features when available.
Do we need llms.txt?
Google says it does not use llms.txt for Search visibility. Evaluate other platforms against their own current documentation. There is no universal requirement and no guaranteed ranking effect.
Practical guidance from PCube Innovations. Examples are illustrative. Platform features and your requirements can change; confirm the details before choosing a solution.