LLM Search Lab

Most brands overlook the humble “Customer Questions & Answers” section on Amazon — but it’s a quiet powerhouse for Rufus visibility. By seeding relevant, natural questions and authoritative brand answers (e.g., “Is this caffeine-free?”), you can feed Amazon’s semantic engine with structured attribute data that strengthens your product’s discoverability. This tactic isn’t about human readers, it’s about helping Rufus and Amazon’s AI systems better understand and surface your product when users ask related queries. Low visibility to shoppers, high impact for indexing.

The Blog Post Optimisation Guide is a step-by-step framework for making articles both highly readable for humans and easily indexable by AI models. It covers clear structure, semantic enrichment, technical setup (slug, schema, metadata), and provides an optimisation prompt to reformat existing drafts.

Edge SEO (or GEO) is the practice of adding structured, semantically rich information at the CDN or server edge so that AI crawlers can easily interpret your site. It ensures your brand and products are visible in conversational AI search without compromising the human-facing experience.

Gemini 2.5 Flash Image (“Nano Banana”) is Google’s image model for natural-language generation, editing and multi-image fusion with reliable likeness consistency, available via the Gemini app, AI Studio and Vertex AI. For brand/e-commerce teams it speeds creation of consistent PDP/A+ visuals and seasonal variants, describe scenes, iterate small tweaks, and specify ratios/brand colours. Learn more on this chat.

How adding schema markup (FAQPage, Product, Review) can change ChatGPT’s brand recommendations, with before/after outputs and practical takeaways.

Set up a dedicated AI Referrals channel in GA4 and use UTM tags to make sure visits from ChatGPT, Perplexity and other models don’t get lost as “Direct.” Then track them in a simple dashboard so you can see traffic, conversions, and revenue from AI search.

AI shelf space is the new digital endcap: when shoppers ask ChatGPT, Rufus, or Claude for products, only a handful make the shortlist—and that list drives discovery and purchase. Winning those slots depends on structured data, semantic clarity, and reviews, not ads—if you’re not in the answer, you’re not in the basket.

With the new auto-routing system, GPT-5 decides on its own whether to give you a quick answer, run deep reasoning, or search the web. For shopping and research, that means reasoning models by default, the kind that can pull from 100+ sources to build a single answer.

Prompt paths reveal the full journey users take in ChatGPT — from their first query to final brand choice, uncovering the language, concerns, and comparisons that drive purchase decisions. For brands, understanding these conversational flows is key to showing up earlier, staying relevant, and getting chosen in AI-powered search.

LLM visibility is how well your brand or product appears in AI-generated answers from models like ChatGPT, Claude, or Amazon Rufus, based on natural language queries. As these models become the first stop for product discovery, brands that optimise content for LLMs can win trust and attention early. Ignoring this shift risks being invisible at key decision moments and losing ground to more LLM-savvy competitors.

Amazon’s search and recommendation engine isn’t powered by one AI — it’s a layered stack. Understanding how Rufus, Alexa+, and COSMO interact gives brands an edge in visibility, conversion, and long-term growth on the platform.

Google SEO ranks pages for clicks; AI Search (like ChatGPT or Rufus) generates answers. You optimise web content for Google, but structured data, reviews & semantics for AI. If your brand’s not in LLM answers, you’re missing a key discovery channel.