LightSite AI and theCUBE Research announced a new research brief titled “Building Brand Equity in AI Engines: How to Get Recommended by LLMs.” The publication explains how large language models influence brand discovery and why slower moving global brands are already losing ground to smaller, more agile competitors in AI driven search experiences.
Buyers increasingly start with AI assistants and new AI shopping experiences rather than traditional search results, and the brands that appear first in those answers are not always the largest or most established, often it is the exact opposite. Many well known companies still rely on a search strategy built around organic traffic and click based engagement, while smaller brands invest in focused, conversational and authentic content and cleaner data structure that AI systems can understand and trust easily.
“In AI search, the playing field is flatter than many people think,” said Stas Levitan, founder of LightSite AI. “A smaller, focused brand with a clear point of view, strong expert content and honest language can be easier for an AI system to trust and cite than a large brand with vague messaging, a confusing variety of products and outdated site architecture. This is a once in a decade opportunity for up and comers, and a real risk for anyone who assumes that they do not have to adapt to the new reality of AI search.”
Large brands face two linked gaps. The first is a content gap, where mid market and enterprise companies publish material that is polished but generic, written more for slides than for real conversations. The second is a technology gap, where product information, customer stories and company data are not exposed in structured, machine readable formats such as schema markup and product knowledge graphs. As AI systems rely more on entity clarity, transparency and consistent narratives, these gaps make it easier for smaller, more focused players to become the default answer for niche, high intent queries – something that was nearly impossible in regular Google search.
The research points to a growing pattern. Newer vendors that speak plainly about specific use cases, show measurable outcomes and maintain consistent expert voices are picked up more quickly by AI systems. They tend to use their own names in citations, appear in transcripts, podcasts and analyst discussions, and keep their claims close to real customer outcomes. By contrast, legacy brands often spread their story across many disconnected campaigns or rely on generic positioning statements, which can result in a weaker signal in AI discovery.
To help brands respond in a structured way, the brief introduces a four layer AI Engine Optimization framework and the AEO Advantage Index. Rather than staying at a theoretical level, the methodology focuses on a small number of practical steps. It benchmarks AEO readiness through an assessment across 19 attributes aligned with how AI learns, retrieves and ranks brands. Based on that assessment, it defines targeted strategies and 90 day action plans to strengthen weak signals and amplify authority in the areas that matter most.
The approach also includes a clear content track. It outlines how organizations can feed AI engines with new, authentic material that reflects a consistent brand narrative, using prompt libraries and product knowledge graphs. In parallel, it describes how to design AI guided buyer journeys so that once a brand gains visibility inside AI answers, that discovery can lead to engagement, evaluation and demand rather than a single isolated mention.
LightSite AI supports this work with a platform that builds machine readable data layers and tracks brand performance in AI search – a unique, patent pending technology. The platform helps organizations make their sites easier for AI systems to understand and cite, and allows teams to monitor how often brand entities, products and experts appear in AI generated responses. More information is available at LightSite AI.
Marketing teams can also explore AI readiness tools from LightSite AI, including Generative Engine Optimization utilities on the tools page at LightSite AI – Test Your AI Search Readiness, which help organizations check AI crawlability, structured data coverage and other technical foundations required for modern discovery. Further details on the research and methodology can be found through theCUBE Research.
The research brief “Building Brand Equity in AI Engines: How to Get Recommended by LLMs” and additional information on the AEO Advantage Index are available from theCUBE Research and LightSite AI.
About LightSite AI
LightSite AI helps businesses and digital platforms improve how they are recognized and represented in AI powered search. The platform builds structured data layers and provides analytics that measure visibility, accuracy and authority across major AI systems, enabling organizations to understand and improve how they appear inside AI generated answers.
About theCUBE Research
theCUBE Research is an independent research and advisory firm focused on the business impact of artificial intelligence, cloud and data technologies. The firm combines analyst insight, event coverage and original research to help technology leaders understand market shifts, evaluate emerging solutions and design data driven strategies for growth.
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Company Name: LightSite AI
Contact Person: Stas Levitan
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Country: Israel
Website: https://www.lightsite.ai/
