How to Optimise for AI Search: A Practical Guide for Marketers and Founders

Most SEO advice still assumes Google is the only game in town. It is not. A growing share of people are now getting answers directly from ChatGPT, Perplexity, Claude, and Gemini without ever clicking a search result. If your brand is not appearing in those AI-generated answers, you are invisible to a meaningful slice of your potential audience.

Optimising for AI search, sometimes called Generative Engine Optimisation (GEO), is not about gaming an algorithm. It is about making your content easy for language models to understand, trust, and cite. Here is how to approach it practically.

Understand How AI Search Actually Works

Traditional search engines rank web pages. AI search engines synthesise answers from multiple sources and then, sometimes, cite where those answers came from. The underlying models are trained on large corpora of web content, but they also pull from live retrieval when tools like Bing or web browsing are enabled.

What this means practically: your content needs to be both crawlable and comprehensible to a machine trying to construct a clear, confident answer. Vague, marketing-heavy copy that hedges everything performs poorly. Specific, structured, factual content performs well.

AI models favour sources that demonstrate clear expertise on a topic. They tend to cite content that directly answers a question, uses consistent terminology, and avoids ambiguity about what the brand or product actually does.

Write for Clarity and Specificity, Not Just Keywords

Keyword density matters far less to AI models than it does to traditional crawlers. What matters more is whether your content clearly and directly answers the questions your audience is actually asking.

Start by identifying the specific questions people ask when they are in research or buying mode for your category. Then write content that answers those questions explicitly, not buried in preambles or wrapped in qualifications. Lead with the answer. Provide context after.

Use precise language. If you sell project management software for remote engineering teams, say that. Do not write "a flexible solution for modern teams looking to work smarter." AI models cannot confidently cite vague positioning, but they can cite a specific claim like "a Kanban and sprint planning tool designed for distributed software teams."

Structured formats help too. Clear H2 and H3 headings that reflect actual questions, short paragraphs, and the occasional bulleted list all make it easier for a model to parse what your content is about and extract relevant information.

Build Topical Authority on Your Core Subject

AI models do not treat all sources equally. They tend to draw from sources that appear frequently and consistently in connection with a particular topic. This means a single well-optimised page is less powerful than a coherent body of content that covers a subject in depth.

Build out a content cluster around your primary topic. If you are a cybersecurity tool for small businesses, you need content covering password management, phishing prevention, compliance basics, incident response, and so on. Each piece reinforces the overall signal that your site understands this space.

Internal linking matters here. Connect related pieces clearly, and use descriptive anchor text. This helps both crawlers and models understand the relationship between your content and your area of expertise.

Earn Mentions and Citations Across the Web

One of the most important factors in AI visibility is whether your brand is mentioned and discussed in credible, third-party sources. AI models are trained on, and often retrieve from, a broad range of web content including forums, review sites, tech publications, and community discussions.

This makes reputation management a direct SEO lever for AI search. If your brand is being discussed positively on Reddit, referenced in comparison threads on Hacker News, or reviewed on G2, those signals feed into how AI models perceive your credibility and relevance.

This is where community intelligence becomes genuinely useful. Tracking where your brand, product category, or key competitors are being mentioned across these platforms lets you spot opportunities to engage, respond, and earn organic citations in exactly the contexts AI models pay attention to. bing.ly monitors these sources continuously, surfacing mentions, pain-point discussions, and buying signals across Reddit, Hacker News, and review sites so you can act on them in real time.

Use Structured Data and Schema Markup

Schema markup is one of the clearest signals you can send to both traditional search engines and AI systems about what your content is. Organisation schema, FAQ schema, HowTo schema, and Product schema all help models understand context quickly.

Implement structured data on your homepage, key product pages, and any content that directly answers common questions. Keep it accurate and consistent with what is actually on the page. Schema that contradicts page content is worse than no schema at all.

If you have a product with clear, objectively describable features, price ranges, and use cases, encode those explicitly in your structured data. AI models synthesising product comparisons will have a much easier time accurately representing your offering.

Keep an llms.txt File

A relatively new convention, the llms.txt file (placed at yourdomain.com/llms.txt) lets you provide a machine-readable summary of your site's key content and structure for large language models. Think of it as a robots.txt equivalent for AI systems, but rather than restricting access, it guides them toward the most important and accurate information about your brand.

Include a clear description of what you do, your main products or services, key differentiators, and links to your most authoritative content. This is especially useful for brands that are newer or less prominent in training data, as it gives models a reliable fallback reference.

Track Your AI Visibility, Then Iterate

Optimisation without measurement is guesswork. The challenge with AI search is that standard analytics tools do not tell you whether you appeared in a ChatGPT answer. You need to actively check.

The methodical approach is to regularly prompt the major AI tools with the searches your target customers would make and record whether your brand is cited, in what context, and who appears instead of you. This is tedious to do manually at scale, but it is the only way to know if your efforts are working.

bing.ly automates this by continuously monitoring whether your brand appears in AI-generated answers across ChatGPT, Perplexity, Claude, and Gemini. It tracks changes over time, flags when competitors gain or lose visibility, and surfaces the community signals that influence AI perception, all in one dashboard built for founders and small marketing teams.

Optimising for AI search is an ongoing process

The way AI systems retrieve and synthesise information continues to evolve, and what ranks well today may shift as models update. The brands that will maintain strong AI visibility are those that build genuine topical authority, earn organic third-party mentions, and consistently produce clear, specific, well-structured content.

Start with an audit of your existing content against these criteria. Identify your most important product and category pages, rewrite them for clarity and specificity, implement structured data, and begin building your topical cluster. Then measure your AI visibility regularly and adjust.

If you want to make that monitoring less manual, bing.ly gives you the visibility tracking and community intelligence to stay ahead, without needing a full SEO team behind you. Visit bing.ly to see how your brand currently appears across AI search.