GEO & AEO — AI Search Optimisation
Generative engine optimisation (GEO) and answer engine optimisation (AEO) are the practice of structuring a business's online presence so AI answer engines — ChatGPT, Perplexity, Google AI Overviews, Claude — select it as a source and cite it directly in a generated answer, rather than optimising purely for a ranked list of blue links. At Beacon Coders this is a newer and more technical discipline than traditional SEO, and one almost no competitor in this market has built real capability in yet. Retainers start at ₹40,000 / $480 per month with a 6-month minimum, since establishing citation patterns with AI engines takes sustained, structural work rather than a one-time fix.
How AI answer engines actually select sources
AI answer engines do not crawl and rank the way traditional search does. Most combine a retrieval step — pulling candidate content from an index or a live search — with a generation step, where the model synthesises an answer and decides which sources to cite based on how clearly a source's content matches the question and how much the model appears to trust that source's domain. Clarity and extractability matter more here than for traditional SEO: a page that states a fact plainly in one sentence gets quoted; a page that buries the same fact in a long narrative paragraph often does not, even if a human reader would find it just as easily.
Trust signals also work differently. Traditional SEO weighs backlinks heavily. AI engines appear to weigh a wider mix of signals including how consistently a fact appears across multiple independent sources — which is why third-party listicles, comparison articles, and review sites matter more here than owned content alone. If five independent, credible sites state the same fact about your business, an AI engine has more reason to trust and repeat it than if the only source is your own website. This is the single biggest mindset shift GEO requires: you are optimising your presence across the web, not just your own domain, which is also why being mentioned accurately on industry roundups and "best of" lists increases citation odds independent of your own site's SEO. A business with strong owned content but no third-party presence is largely invisible to a model that weighs corroboration.
Structuring for extraction, llms.txt, and crawler policy
Content structured for extraction states the direct answer to a likely question in the first sentence or two of a section, before elaborating — the opposite of a narrative build-up that delays the answer for engagement. FAQ sections with question-shaped headings and direct opening answers are unusually well-suited to this. We also implement llms.txt, an emerging convention that gives AI crawlers a structured summary of what a site contains and where to find authoritative answers, similar in spirit to a sitemap but written for language models rather than search indexers.
Crawler policy is the part competitors get backward most often: blocking AI crawlers (GPTBot, ClaudeBot, PerplexityBot and similar) in robots.txt to prevent training-data collection also blocks the retrieval step many of these same engines use to answer live queries, forfeiting visibility in exactly the channel this service is built to win. We help clients separate the training-data question, a legitimate business decision, from the retrieval-access question, which usually should stay open if being cited matters to the business.
What's included
A citability audit of existing content, testing how clearly key facts are extractable versus buried in narrative prose
A citability audit of existing content, testing how clearly key facts are extractable versus buried in narrative prose
Restructuring priority pages so direct answers appear at the top of relevant sections, in the pattern AI engines cite most reliably
Restructuring priority pages so direct answers appear at the top of relevant sections, in the pattern AI engines cite most reliably
llms.txt implementation, giving AI crawlers a structured map of authoritative content on the site
llms.txt implementation, giving AI crawlers a structured map of authoritative content on the site
Crawler policy review, ensuring robots.txt does not block the retrieval access needed for citation while still controlling training-data collection separately
Crawler policy review, ensuring robots.txt does not block the retrieval access needed for citation while still controlling training-data collection separately
Third-party source mapping
identifying listicles and industry roundups that matter in your category and working toward accurate inclusion in them
Structured data and schema markup that supports machine extraction of facts, not just search engine rich results
Structured data and schema markup that supports machine extraction of facts, not just search engine rich results
Ongoing testing of actual AI engine responses to buyer-relevant questions, tracking whether and how your business is cited over time
Ongoing testing of actual AI engine responses to buyer-relevant questions, tracking whether and how your business is cited over time
Monthly reporting on citation frequency and accuracy across major AI answer engines, alongside the SEO metrics this work also tends to improve
Monthly reporting on citation frequency and accuracy across major AI answer engines, alongside the SEO metrics this work also tends to improve
What's not included
GEO & AEO does not include traditional keyword-ranked SEO, a separate, complementary discipline — see SEO services, which most GEO clients also run. It does not include paid placement in any AI engine's answers, which does not exist as a purchasable product from any major provider. It does not include guaranteed citation, since no one controls what a third-party model generates; we commit to the structural work that measurably improves the odds, not a promised outcome. It does not include original long-form content production — see content marketing.
Frequently asked questions
How we run a GEO & AEO engagement
We start with a citability audit, testing priority pages against real AI engine queries to see whether and how they currently get cited — a different starting point from the ranking-focused audit that opens most digital marketing programmes, though we run both where a client needs both. From there we restructure content for extraction, implement llms.txt and correct crawler policy, and begin third-party outreach in parallel, re-testing actual AI engine answers monthly against a tracked set of buyer questions.