The labels overlap, but the customer need is clear
SEO usually refers to improving discovery in search engines. AEO commonly describes making information useful for answer-focused experiences. GEO and LLMO are used for work intended to improve visibility in generative systems and language-model responses. These labels are not universally standardised, and agencies may use them differently. Ask what specific work and measurement sit behind the terminology.
For a business, the practical aim is straightforward: make accurate information easy to access, understand and verify when people research a relevant question. That starts with explaining who you are, what you provide, whom you serve and what evidence supports your claims. Replacing ordinary website language with technical buzzwords does not make the information more trustworthy or more useful.
Keep the search foundation strong
Google's documentation says the established SEO fundamentals remain relevant to its AI search features. Eligibility depends on the page meeting its search requirements, and inclusion is not guaranteed. Google also states that no special AI markup or new machine-readable file is required for those features. Treat an optional llms.txt file as an experiment in providing context, not a ranking mechanism or universal requirement.
Check that meaningful content is available as text, pages can be reached through normal links and the infrastructure does not accidentally block the crawlers you intend to allow. Review controls for individual platforms rather than assuming all AI-related crawlers serve the same purpose. Search discovery, user-requested retrieval and model training can involve different systems and policies.
Write answers that stand on their own
Use a clear question or descriptive heading, followed by a direct answer and the details that make it useful. A paragraph should identify its subject without depending entirely on a heading several sections above. Explain terms, include conditions and state limitations where they change the advice. This also helps a human reader who arrives halfway through an article.
Make comparisons fair and specific. Explain when an approach fits and when it does not. Use examples that are clearly labelled as examples instead of implying they are documented client results. Link factual claims to primary sources where appropriate, and separate your suggested workflow from a platform's actual requirements. Clear attribution gives readers a way to evaluate the information themselves.
Build evidence around the business
Keep the company name, services and contact details consistent across the website and the profiles you control. Publish approved project information with enough context to explain what was done. Add real authorship or editorial responsibility where available, but do not fabricate expert biographies, awards or credentials to make the site appear more authoritative.
When the business has original insight, make it explicit: a documented process, a useful template, a carefully explained comparison or permission-based research. A page that merely repeats generic advice gives people little reason to cite it. Preserve the limits of any evidence, especially when a result came from a specific period, audience or campaign. Accuracy should survive being read outside the original page layout.
Measure visibility without pretending it is stable
Track useful referrals, relevant enquiries and observed appearances in AI experiences where the platform provides meaningful reporting. For manual checks, record the question, system, date and visible sources. Responses can vary with context and over time, so a single answer is not a dependable ranking. Avoid turning a few selected screenshots into a claim of universal visibility.
Use those observations to identify unclear information or missing evidence, then improve the underlying pages. The most useful programme combines technical access, strong explanations, credible business information and measured iteration. Nobody can promise that a particular assistant will mention your company for every relevant request. The defensible promise is careful work that makes your business easier for people and systems to understand.
A practical next step
Ask a prospective optimisation partner to distinguish deliverables from outcomes. A deliverable might be repairing access, improving a service explanation or documenting evidence. An outcome might be a relevant referral or an observed citation. The first is work the team controls; the second depends partly on external systems. Keeping that distinction in the proposal makes progress easier to evaluate without mistaking a technical checklist for guaranteed exposure.