From keyword SEO to AI search optimization
AI search optimization is the practice of understanding, measuring and improving how large language models and AI-powered discovery engines perceive, describe and recommend a brand across conversational search experiences. Instead of ranking for short, static keywords, marketers now need to influence how models assemble answers, select citations and choose which products or services to highlight in a single, compressed response. This shift is driven by rising “zero-click” behavior, where users rely on AI summaries or overviews rather than clicking through to websites, and by growing trust in AI-generated guidance during product research. As Google, Amazon and standalone tools such as ChatGPT, Gemini, Claude and Perplexity add richer AI layers, traditional SEO signals still matter, but they are no longer enough. The new challenge is shaping a brand’s presence inside the models that power these curated responses.
NeuroRank and the rise of Model Preference Engineering
NeuroRank positions itself as an enterprise-grade generative engine optimization platform built around “Model Preference Engineering,” offered through subscriptions starting at USD 225 (approx. RM1,050) per month. Its workflow goes beyond monitoring: it deconstructs how AI systems talk about a brand, diagnoses gaps or misrepresentations, prescribes content and data fixes, conditions models via owned, earned and third‑party sources, then tracks month‑on‑month lift. NeuroRank defines this practice as Large Language Model Optimization, focusing on how AI models perceive, cite and recommend brands across engines such as ChatGPT, Gemini, Claude and Perplexity. In one 90‑day engagement, a leading BFSI brand improved AI visibility by 30 percent and citation frequency by 12 percent, while an FMCG brand recorded a 47 percent visibility gain. For marketers worried about disappearing from AI answers, platforms like NeuroRank act as brand visibility tools tuned to LLM search visibility rather than classic page rankings.
Emna.ai and LLM search visibility for product discovery
Tradedoubler’s Emna.ai tackles the same problem from a performance and affiliate marketing angle, focusing on how products show up in AI-led recommendation journeys. As consumers ask conversational questions such as “what’s the best skincare for sensitive skin?”, discovery is shifting from keyword lists to shortlists built by LLMs. Emna.ai measures a brand’s share of voice across AI-generated answers, shows which domains and articles are being cited, and distinguishes between citations and actual recommendations. According to Tradedoubler, users treat LLM outputs as guided conversations, returning with follow‑up questions rather than scanning long link lists, while exposure shrinks to a few citations and even fewer direct suggestions. Emna.ai helps brands see which content and partners are influencing that outcome, so they can refine product data, review coverage and publisher strategies. In doing so, it turns opaque AI-powered discovery into something that can be analyzed and optimized.
Google, Amazon and the new AI-led product search battleground
Major platforms are weaving generative AI into search and shopping, reshaping the funnel before a user ever reaches a retailer’s site. Google’s AI Overviews now sit above organic and paid listings, while shoppers increasingly begin with LLMs such as ChatGPT or Perplexity for side‑by‑side comparisons, reviews and recommendations. At the same time, Amazon is experimenting with LLM-powered exploration tools that answer natural language questions about features, fit and value, compressing the old multi‑click research journey into a few AI‑generated summaries. This fuels the broader “zero‑click” trend, where users feel they have enough information from the overview alone. As curated answers replace paginated results, the window for exposure narrows: many brands never make it into the shortlist that shapes preference. The competitive arena is shifting from search results pages to the internal logic of AI models that decide what and who to feature.
From keywords to preferences: what marketers should do next
The move from keyword-based SEO to preference-based AI search optimization requires new playbooks, not minor tweaks. Traditional best practices still matter, because high‑quality, relevant content remains a core signal, but they are now a starting point. Brands need tools that reveal how models describe them, which entities they associate them with, and whether they are cited or recommended for the queries that matter. That is the gap platforms like NeuroRank and Emna.ai aim to fill, combining generative engine optimization with diagnostic reporting on LLM search visibility. Marketers should treat AI systems as reputation layers: train them through accurate product data, rich FAQs, expert content, credible reviews and consistent third‑party coverage. The brands that adapt fastest will be those that treat model preferences as a new channel, integrate AI-focused visibility metrics into their reporting, and invest in ongoing conditioning rather than one‑off SEO campaigns.