Generative Search Creates a New Battle for Brand Visibility
AI search visibility is the practice of understanding and improving how generative search engines and large language models describe, cite, and recommend brands when users ask conversational questions instead of clicking traditional search links. As AI summaries, Overviews, and assistants compress browsing into a single answer, the competition for inclusion in that answer has become intense. Gartner predicts that traditional search volume will fall by 25% by 2026, while Bain’s research shows that 80% of consumers rely on “zero-click” results at least 40% of the time. At the same time, SparkToro reports that nearly six in ten Google searches in major markets now end without a click. Together, these shifts turn generative engine optimization into a strategic priority, pushing marketers to seek new brand ranking tools that can expose and improve how models talk about their products.
NeuroRank.ai Brings Model Preference Engineering to the Mass Market
NeuroRank.ai, developed by Pulp Strategy Communications, is opening its patent-pending AI visibility intelligence platform as SaaS, making its Generative Engine Optimization system available from USD 225 (approx. RM1,035) per month. The platform is built around Model Preference Engineering, a continuous monthly practice that diagnoses how AI models perceive a brand, prescribes fixes, conditions those models across owned, earned, and third‑party sources, and then tracks visibility lift as the models recalibrate. Unlike tools that only monitor results, NeuroRank claims a five‑step methodology that deconstructs, diagnoses, prescribes, conditions, and tracks AI search visibility end to end. Founder Ambika Sharma describes it as a way to “see, govern, and command how AI talks” about a brand, after stress‑testing with 150 brands across 65 industries. For marketers, this moves GEO from guesswork into a repeatable, data‑driven workflow.
Tradedoubler’s Emna.ai Targets AI Search in the Shopping Journey
Performance marketing network Tradedoubler is introducing Emna.ai, a platform designed to improve a brand’s visibility inside AI search environments where product discovery is rapidly shifting. Director of AI Corin Ward notes that consumers now start with conversational prompts such as “what’s the best skincare for sensitive skin?”, using tools like Google’s AI Overviews, ChatGPT, and Perplexity as the first stop in their shopping journey. Users then treat results as a guided dialogue, refining the answer through follow‑up questions rather than scanning long lists of links. According to Yext’s 2025 study, 62% of consumers already trust AI to guide brand decisions, even if many still cross‑check recommendations. Emna.ai aims to help brands understand their share of voice in these LLM environments, identify which content drives inclusion in AI answers, and inform generative engine optimization efforts so that brands appear inside the shortlists that matter.
BERA.ai Connects LLM Brand Rankings to Revenue Impact
BERA.ai is expanding its brand measurement platform with LLM Brand Rankings, a feature that shows how leading language models such as Gemini, ChatGPT, and Claude rank brands across categories. This capability sits alongside BERA’s existing BERA Score and Love Curve metrics, giving marketers one view of how brand equity and AI search visibility move together or diverge. The tool lists how LLMs position a brand, highlights the key content sources shaping that view, and links these rankings to revenue and growth through BERA’s Brand‑to‑Business connection. As Chief Customer Officer Kraig Schulz puts it, brand lives “wherever consumers make decisions, and today, more of those decisions start with a prompt to an LLM.” With GEO integrations built in, BERA.ai can recommend actions to improve both LLM brand rankings and business outcomes, turning AI visibility from a black box into a measurable driver of demand.
From SEO to GEO: What Marketers Need to Do Next
Together, NeuroRank.ai, Emna.ai, and BERA.ai signal a wider shift from classic SEO to generative engine optimization, where the focus is not only on ranking in search results but on shaping how LLMs summarise and recommend brands. Many GEO foundations still overlap with SEO: relevant, high‑quality, well‑structured content that aligns with real user questions remains essential. Google’s own guidance on generative AI features confirms that its AI experiences are still rooted in core ranking and quality systems. What changes is the measurement layer. Marketers now need tools that expose LLM brand rankings, reveal which pages and third‑party sources influence AI answers, and connect those signals to revenue. As AI‑driven, zero‑click behaviour grows, brands that invest early in AI search visibility are more likely to secure a place in the compressed shortlist that guides consumer decisions.