LLM Optimisation
AI Search 101
What it is, why it matters, and how to win the new shelf space
19 October 2025
12 min read
TL;DR
AI search is how people now find, compare, and decide inside AI assistants and chat experiences not just on search engines. Instead of ten blue links, users get a reasoned answer, a shortlist, and a nudge to buy. Winning here means making your products machine readable, trustable, and recommendable across models like ChatGPT, Gemini, Claude, Copilot, Perplexity, and Amazons Rufus. At LMO7 we call this owning your AI shelf space.
What is “AI search”?
AI search is the shift from keyword matching to reasoning first discovery. A user asks a natural question, the model plans the task, pulls signals from multiple sources, and returns a contextual answer rather than a list of links. It blends search, comparison, and buying advice into one flow.
Key traits
Conversational queries feel like chatting to a smart shop assistant.
Goal oriented models plan steps define needs → shortlist → trade offs → pick .
Multi source blends brand sites, retailer graphs, reviews, specs, PDFs, and product feeds.
Citations and actions links, buttons, and product cards appear inside the answer.
Memory follow up questions refine the result.
Why it matters now
Attention has moved more journeys begin in AI environments.
The result is compressed fewer visible slots. If you are not in the answer, you are invisible.
Speed to purchase clearer content and stronger proof now translate faster to revenue.
Retailer AIs are rising products surface on reasoning paths, not keywords alone.
How AI search works simple view
Intent “Best lightweight sunscreen for long runs in humid weather, under £20.”
Reasoning path constraints SPF, sweat durability, price, sensitivity.
Signal gathering reads PDPs, specs, FAQs, reviews, brand docs, schema.org.
Synthesis produces a shortlist with trade offs and “why”.
Action provides links, add to basket, store availability, or follow ups.
If your content does not express attributes, outcomes, and proofs clearly, you are skipped.
The LMO7 framing AI shelf space
Your job is to earn and defend placement inside AI answers. We track this as Share of Model, the percent of AI answers that name or feature your brand vs competitors. Think SERP share, but for AI conversations.
The Seven Pillars LMO7
Signal Architecture and Baseline Audit
Audit brand visibility across ChatGPT, Gemini, Claude and Perplexity.
• Enrich and standardise product metadata across all endpoints.
• Implement structured data and ensure brand consistency.
• Output A unified data foundation and baseline “signal map”.
Language Model Alignment
Define query clusters with Lexym.
• Optimise copy for natural, conversational phrasing.
• Expand content to match consumer intent.
• Output Language tuned assets aligned with LLM retrieval.
Contextual Authority
Secure mentions on trusted editorial and review sites.
• Publish crawlable FAQs and brand knowledge content.
• Seed or support expert and UGC discussions.
• Output Authority footprint across model referenced sources.
Model Surface Monitoring
Track brand recall and competitor presence across models.
• Benchmark shifts against the baseline audit.
• Flag visibility drops or misattributions.
• Output A live dashboard of brand vs competitor mentions.
Optimisation Loops
Run monthly tests to measure ranking shifts.
• Compare on site and off site changes with model baselines.
• Trial A B content variants on high impact queries.
• Output Refined assets and stronger semantic signals.
Visibility Leverage Points
Pinpoint high volume or high impact queries.
• Target authority mentions and influencers.
• Syndicate content across influential channels.
• Output Priority actions with outsized visibility gains.
AI Native Brand Positioning
Shape USPs as direct answers to model queries.
• Refine a natural, conversational brand voice.
• Frame the brand narrative in model agnostic terms.
• Output A durable, AI native brand story across models.
Examples of AI style queries to pre answer
“Sunscreen that will not sting eyes on a marathon.”
“Steel toe boots that are ESD compliant for aerospace tooling.”
“Hydration tablets for heavy sweaters, zero caffeine, UK delivery tomorrow.”
“Compare these two models for plantar fasciitis, pros and cons for 10 hour shifts.”
Metrics that matter
Share of Model percent of relevant AI answers that feature your brand.
Coverage percent of priority prompts where you appear with a positive, actionable mention.
Sell through Lift revenue change on target SKUs following content and structure updates.
Common pitfalls
Keyword nostalgia stuffing terms without answering real missions.
Inconsistent specs conflicting weights, materials, or claims across channels.
No proof outcomes stated without tests, certifications, or quantified results.
Hidden answers burying crucial info in images only or vague lifestyle copy.
Slow iteration discovering gaps but not re publishing quickly.
FAQ
Is this just SEO with new clothes
No. Classic SEO is about ranking pages. AI search is about earning inclusion in answers. Structure and proof beat word count.
Do backlinks still matter
Authority helps, but coherent product data and evidence are what models quote.
What is the fastest win
Add FAQ style answers to the exact buyer questions you see, mirror them on Amazon bullets and A+, and make them machine readable with schema.
AI search is how people now find, compare, and decide inside AI assistants and chat experiences not just on search engines. Instead of ten blue links, users get a reasoned answer, a shortlist, and a nudge to buy. Winning here means making your products machine readable, trustable, and recommendable across models like ChatGPT, Gemini, Claude, Copilot, Perplexity, and Amazons Rufus. At LMO7 we call this owning your AI shelf space.
What is “AI search”?
AI search is the shift from keyword matching to reasoning first discovery. A user asks a natural question, the model plans the task, pulls signals from multiple sources, and returns a contextual answer rather than a list of links. It blends search, comparison, and buying advice into one flow.
Key traits
Conversational queries feel like chatting to a smart shop assistant.
Goal oriented models plan steps define needs → shortlist → trade offs → pick .
Multi source blends brand sites, retailer graphs, reviews, specs, PDFs, and product feeds.
Citations and actions links, buttons, and product cards appear inside the answer.
Memory follow up questions refine the result.
Why it matters now
Attention has moved more journeys begin in AI environments.
The result is compressed fewer visible slots. If you are not in the answer, you are invisible.
Speed to purchase clearer content and stronger proof now translate faster to revenue.
Retailer AIs are rising products surface on reasoning paths, not keywords alone.
How AI search works simple view
Intent “Best lightweight sunscreen for long runs in humid weather, under £20.”
Reasoning path constraints SPF, sweat durability, price, sensitivity.
Signal gathering reads PDPs, specs, FAQs, reviews, brand docs, schema.org.
Synthesis produces a shortlist with trade offs and “why”.
Action provides links, add to basket, store availability, or follow ups.
If your content does not express attributes, outcomes, and proofs clearly, you are skipped.
The LMO7 framing AI shelf space
Your job is to earn and defend placement inside AI answers. We track this as Share of Model, the percent of AI answers that name or feature your brand vs competitors. Think SERP share, but for AI conversations.
The Seven Pillars LMO7
Signal Architecture and Baseline Audit
Audit brand visibility across ChatGPT, Gemini, Claude and Perplexity.
• Enrich and standardise product metadata across all endpoints.
• Implement structured data and ensure brand consistency.
• Output A unified data foundation and baseline “signal map”.
Language Model Alignment
Define query clusters with Lexym.
• Optimise copy for natural, conversational phrasing.
• Expand content to match consumer intent.
• Output Language tuned assets aligned with LLM retrieval.
Contextual Authority
Secure mentions on trusted editorial and review sites.
• Publish crawlable FAQs and brand knowledge content.
• Seed or support expert and UGC discussions.
• Output Authority footprint across model referenced sources.
Model Surface Monitoring
Track brand recall and competitor presence across models.
• Benchmark shifts against the baseline audit.
• Flag visibility drops or misattributions.
• Output A live dashboard of brand vs competitor mentions.
Optimisation Loops
Run monthly tests to measure ranking shifts.
• Compare on site and off site changes with model baselines.
• Trial A B content variants on high impact queries.
• Output Refined assets and stronger semantic signals.
Visibility Leverage Points
Pinpoint high volume or high impact queries.
• Target authority mentions and influencers.
• Syndicate content across influential channels.
• Output Priority actions with outsized visibility gains.
AI Native Brand Positioning
Shape USPs as direct answers to model queries.
• Refine a natural, conversational brand voice.
• Frame the brand narrative in model agnostic terms.
• Output A durable, AI native brand story across models.
Examples of AI style queries to pre answer
“Sunscreen that will not sting eyes on a marathon.”
“Steel toe boots that are ESD compliant for aerospace tooling.”
“Hydration tablets for heavy sweaters, zero caffeine, UK delivery tomorrow.”
“Compare these two models for plantar fasciitis, pros and cons for 10 hour shifts.”
Metrics that matter
Share of Model percent of relevant AI answers that feature your brand.
Coverage percent of priority prompts where you appear with a positive, actionable mention.
Sell through Lift revenue change on target SKUs following content and structure updates.
Common pitfalls
Keyword nostalgia stuffing terms without answering real missions.
Inconsistent specs conflicting weights, materials, or claims across channels.
No proof outcomes stated without tests, certifications, or quantified results.
Hidden answers burying crucial info in images only or vague lifestyle copy.
Slow iteration discovering gaps but not re publishing quickly.
FAQ
Is this just SEO with new clothes
No. Classic SEO is about ranking pages. AI search is about earning inclusion in answers. Structure and proof beat word count.
Do backlinks still matter
Authority helps, but coherent product data and evidence are what models quote.
What is the fastest win
Add FAQ style answers to the exact buyer questions you see, mirror them on Amazon bullets and A+, and make them machine readable with schema.