# AEO Basics - Why Do Different AI Models Recommend Different Brands? By Jeremy Prasetyo, Co-CEO, Lanesra AI · 2026-08-20 · AEO Basics > The same prompt can produce different brand recommendations across AI search engines. Here is why retrieval, source selection, query fan-out, context, and model behavior change the answer. Source: https://lanesra.ai/blog/aeo-basics-why-do-different-ai-models-recommend-different-brands Ask the same question in ChatGPT, Gemini, Perplexity, or another AI search engine and you may get a different shortlist of brands. That does not necessarily mean one engine is right and another is wrong. It means there is no single universal AI ranking. The short answer Different AI search products can interpret the prompt differently, run different follow-up searches, retrieve different sources, rank that evidence differently, and use different models to generate the final answer. **The same brand can therefore be highly visible in one engine and nearly absent in another.** ## There Is No Single AI Ranking Traditional search trained marketers to think in positions: number one, number two, number three. AI search is less uniform. What the user sees is the result of several systems working together. When marketers say “different AI models,” they often mean the entire AI search product. The underlying language model matters, but so do the search index, retrieval system, query expansion, ranking logic, freshness controls, user context, and product rules around how an answer should be composed. Same prompt. Different recommendation stack. 1\. InterpretationHow the engine understands the user's intent and what constraints it extracts. 2\. Query fan-outWhich related searches or sub-questions it creates before answering. 3\. RetrievalWhich pages, databases, product feeds, or indexed sources are available to it. 4\. Evidence rankingWhich retrieved sources appear most relevant and trustworthy for this question. 5\. GenerationHow the model turns that evidence into a shortlist, comparison, or recommendation. ## 1\. They Search Differently AI engines do not necessarily turn your prompt into the same searches. ChatGPT Search can rewrite a user prompt into one or more targeted queries and may run additional searches after reviewing the first results. Google says AI Mode and AI Overviews can use query fan-out across related subtopics and data sources. That matters because one engine might explore “best CRM for startups,” while another may break the same prompt into pricing, integrations, reviews, implementation effort, and company size. Different searches create different candidate brands. ## 2\. They Do Not See the Same Web Every AI search product has its own retrieval stack. Some rely heavily on a search index, others combine search providers, proprietary indexes, structured product data, or specialist sources. Even within one provider, configurations can change which results are retrieved. If your brand is strong in the sources one engine retrieves but weak in the sources another engine prefers, your visibility will differ. AI system What it tends to rely on **ChatGPT** Web search results, reviews, retailer pages, and synthesis across multiple searches. **Perplexity** High-authority domains, publisher pages, and cited web sources. **AI Overviews** Google Search results, query fan-out, and Google-grounded sources. **Gemini** Google-grounded web sources and retrieved context. **Claude** Its model knowledge plus external web search when search is enabled. **Grok** X posts, live web sources, and recent web context. Illustrative simplification for article use. Source preferences vary by prompt, model, search mode, region, and product configuration. Illustrative simplification: each AI system tends to retrieve, ground, and prioritize different source sets. ## 3\. They Weight Evidence Differently Authority An engine may give more weight to established or highly trusted sources for a sensitive recommendation. Freshness Recent product changes, pricing, reviews, or documentation may matter more for some queries. Specificity A niche page that directly matches the use case can beat a larger generic page. Consensus Repeated claims across independent sources can strengthen the evidence behind a recommendation. The exact weighting is proprietary and changes over time. That is why AEO should not be reduced to one “ranking factor.” ## 4\. Context Can Change the Answer Recommendations can also depend on the user. Location, language, remembered preferences, the conversation so far, budget, company size, and other constraints can change both the search and the final shortlist. A prompt such as “What is the best accounting software?” is broad. “What is the best accounting software for a 15-person ecommerce company in Germany that needs Shopify integration?” is a different recommendation problem. ## 5\. The Model Still Has to Make a Choice Retrieval does not automatically determine the final answer. The model still has to synthesize the evidence, decide what is relevant, resolve conflicts, and present a recommendation. Two engines can retrieve overlapping sources and still produce different conclusions. > **The practical consequence:** being visible in one AI engine is not proof that your AEO strategy is working everywhere. AI visibility has to be measured engine by engine, prompt by prompt, and over time. ## What Marketers Should Measure Metric What to compare across AI engines **Mention rate** How often your brand appears at all. **Recommendation rate** How often the engine actively recommends you. **Answer position** Where your brand appears relative to competitors. **Sentiment** How the engine describes your strengths, weaknesses, and fit. **Citations** Which sources are supporting the answer in each engine. **Prompt coverage** Which commercially relevant questions you win or lose. ## The AEO Lesson You are not optimizing for one chatbot. You are building an evidence footprint that multiple AI systems can understand, retrieve, and trust. That means strengthening your own product and category pages, documentation, structured data, third-party authority, reviews, comparisons, and the consistency of how your brand is described across the web. Then you need to measure where each AI engine actually picks you - and where competitors are still winning. Lanesra is built around that multi-engine view: measure your brand across AI search, compare recommendations and citations, and identify where the evidence gap is different from one engine to another. ### One prompt can have several winners [**Start free with Lanesra**](https://lanesra.ai)