AI Search Optimization (AEO)
Be the answer, not just a result. Get cited by AI search engines.
How AI Search Engines Pick Their Answers
When a user asks ChatGPT, Perplexity, or Google's AI Overviews a question, the system does not search the live web in real time and pick the top-ranked result. It synthesises an answer from a training corpus and retrieval index shaped by three primary signals: the authority and trustworthiness of the source domain, the recency and specificity of the content, and the structural clarity that allows a language model to extract a coherent, accurate answer. A page that ranks well in traditional Google search may still be invisible to AI systems if it lacks the entity clarity and structured data that LLMs require to confidently cite it.
Perplexity's retrieval model prioritises sources with high citation density — pages that other authoritative sources reference and quote. ChatGPT's browsing mode (and its training corpus) heavily weights domain reputation signals similar to Google's E-E-A-T framework. Google's AI Overviews pull from pages already in the top positions for a query, but with an additional preference for content formatted as a direct, self-contained answer — not content that requires the user to read five paragraphs before reaching the point. Understanding these distinct sourcing mechanics is the starting point of every AEO engagement we run.
Structured Content, Schema & Citation Building
AI search engines parse content most reliably when it is formatted with machine-readable precision. FAQ schema and HowTo schema are not just traditional SEO tools — they are explicit invitations to an LLM's retrieval layer, providing cleanly bounded question-answer pairs that a model can extract without interpretation. We audit your existing content for structured data coverage and implement the schema vocabulary most relevant to your sector: FAQ schema for informational content, Service schema for your core offerings, LocalBusiness schema for location authority, and Speakable schema for voice and AI assistant surfacing.
Citation building for AI search is fundamentally different from traditional link building. LLMs are trained on corpora that over-represent certain authority domains — Wikipedia, major news outlets, government sources, and established industry publications. Getting your brand, data, or expert opinion cited by these sources creates training signal that accumulates over model update cycles. We identify citation opportunities through expert commentary placements, data-driven content that earns press pickups, and structured outreach to publications whose content is disproportionately represented in LLM training data. This is a slower, more editorially intensive process than traditional link building — and significantly more durable.
Brand Mention & Knowledge Panel Strategy
Google's Knowledge Graph is the structured database that powers both traditional Knowledge Panel cards and many of the entity relationships that AI systems use to understand who a business is, what it does, and how credible it is. A business without Knowledge Graph presence is an unknown entity to AI — it may be mentioned in content, but it cannot be reliably cited with confidence by an AI system that cannot verify the entity. Establishing Knowledge Graph presence for businesses in Bangladesh requires a coordinated strategy: consistent entity data across Wikidata, structured markup on your own domain, and brand mentions from authoritative sources that associate your business name with specific expertise claims.
We track brand mention velocity across authoritative sources as a primary KPI — not just search rankings. As AI Overviews and AI assistant usage grow among Bangladesh's expanding digital population, the businesses that have invested in entity authority and citation presence will have an insurmountable head start over those who waited. Our AEO engagements are designed to begin building that authority now, while the competitive field in Bangladesh is still relatively sparse, and before the AI search landscape consolidates around a smaller set of established authorities.
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Service Parameters & FAQs
How is AEO different from traditional SEO?
Traditional SEO optimises for Google's blue-link ranking algorithm — the goal is to appear in a list of search results and earn a click. AEO (Answer Engine Optimisation) optimises for systems that synthesise a direct answer without necessarily sending a click — ChatGPT, Perplexity, and Google AI Overviews. The signals differ: AEO prioritises entity authority, structured data, citation in authoritative external sources, and content formatted as direct answers rather than keyword-dense articles. Practically, an effective AEO strategy also improves traditional SEO performance — but the reverse is not always true.
How long before we start appearing in AI search answers?
Google AI Overviews typically respond fastest to structured data and content format improvements — clients with existing domain authority often see AI Overview appearances within 6 to 12 weeks of on-page AEO optimisation. Perplexity and ChatGPT citations are more dependent on external publication and citation building, which operates on a 3 to 6 month horizon. Knowledge Graph entity establishment is the longest-cycle component, typically requiring 4 to 8 months to generate a confirmed Knowledge Panel. We report on all three channels separately so you can track progress with appropriate timeline expectations for each.
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