Start with the question your customer actually asks.
Imagine someone asking an assistant to find a company that can connect their booking system to their CRM. A homepage that says ‘innovative digital solutions’ gives them very little to work with. A page that explains the systems you connect, the problem you solve and the limits of that service is more useful to the person making the decision.
Our starting point is a small set of real buying questions. What is the service for? Who is it for? What needs to be in place? What happens next? Answer them with specifics you can stand behind.
Check access before adding an AI layer.
Google’s AI search features rely on its existing search systems. Useful original content and a clear, crawlable site remain the foundation; inclusion is not guaranteed. Check indexing and access controls before treating a missing mention as a copywriting problem.
Search access and training access are separate choices.
For ChatGPT search, OpenAI documents OAI-SearchBot. GPTBot serves a different purpose: possible use in model training. Their permissions are independent. Review your robots rules and firewall together; a permitted crawler still needs to reach the page.
Source: OpenAI crawler documentation
Where does llms.txt fit?
The llms.txt proposal describes a concise Markdown guide to a site: a project name, short context and organised links to useful content. It can help systems that support it find documentation. Keep the linked information current and treat the file as a maintained directory.
Source: The llms.txt proposal and format
A file is not a recommendation guarantee.
Google explicitly says it does not use llms.txt to improve Search visibility or rankings. Adding it does not replace accessible pages, useful explanations or evidence of your work.
Before spending money, ask for a baseline, the pages that need improvement and a way to measure relevant visits or enquiries. A screenshot of one favourable AI answer is a sample, not a dependable acquisition channel.