LLM Optimisation
Inside Lexem.io: How Our Query Clustering Maps Real Buying Intent
Lexem.io is LMO7’s query-clustering engine. You start broad (category, geography, funnel stage), add your high-converting keywords, and the tool generates human-validated, AI-expanded prompt sets grouped by intent. You curate, save, visualise as clusters, and extend them into prompt paths that mirror how shoppers move from research → comparison → purchase. These sets then power our Share-of-Model tracking and your content, PDPs, FAQs, and retail media.
7 November 2025
8 min read
What Lexem.io is (and why it exists)
Search has shifted from exact keywords to intent. People ask assistants open questions, get reasoning-led answers, then jump to marketplaces to buy. Lexem.io maps the questions that matter for your category so your brand shows up consistently in Google, ChatGPT, and Amazon Rufus.
The Lexem.io flow
1) Start broad, then narrow
Category: pick a high-level space (e.g., Fitness).
Subcategory: choose (or type) specifics (e.g., Exercise bikes).
This “top-down” start ensures the clusters reflect the full market, not just your current copy.
2) Set geography
Global, a region, or hyper-local (e.g., UK, New York).
Localising up front captures real phrasing, regulations, and delivery constraints.
3) Add seed keywords
Paste high-converting terms from Google and Amazon.
These seeds anchor the model in proven demand while keeping room for new, adjacent intents.
4) Choose the funnel stage
Research (upper funnel awareness)
Comparison (mid-funnel evaluation)
Buying (lower-funnel purchase)
This focuses generation and clustering on the moments that matter for your goal.
5) Generate recommended prompts (dual data source)
Lexem.io blends:
ChatGPT expansion for coverage and paraphrases.
Wild-chat real queries (authentic human wording).
Together they produce a thematic map of what people actually ask, not just what keywords suggest.
6) Intent-based grouping (the clusters)
Prompts are grouped into:
Problems & solutions
Best of / top recommendations
Comparisons & reviews
General information
Buying & purchase
You can toggle branded vs unbranded views to separate category demand from brand-led demand.
7) Curate the set
Accept / reject recommended prompts.
Add your own where you see gaps or new angles.
This editorial pass locks in relevance and removes noise.
8) Save & visualise
Saved prompts form a query set.
A bubble graph shows clusters around the core theme (size by volume/priority; proximity by intent similarity).
This makes it obvious where you’re strong, thin, or conflicting.
9) Build prompt paths (how shoppers progress)
For any saved query, generate a likely follow-on sequence to mirror real behaviour.
Example (UK, Exercise bikes):
“Best exercise bike for small flats”
“Magnetic vs belt drive for noise at night”
“Watt targets for HIIT vs steady state”
“Assembly required and doorway fit UK”
“Return policy and warranty on Amazon”
These paths become briefs for content, PDP bullets/A+, FAQs, and retail-media copy.
Why clustering beats keywords (for AI search)
Intent over exact match: assistants understand paraphrases; clusters ensure coverage even when wording changes.
Constraint capture: models weigh attributes (noise, footprint, delivery) more than literal tokens.
Consistency across surfaces: one cluster set feeds Google, ChatGPT, Amazon, and your site so claims don’t conflict.
What you get out of Lexem.io
Publish-ready prompt sets by funnel stage and geography.
Cluster visualisation to guide content and PDP prioritisation.
Prompt paths to script Q&A, how-tos, comparisons, and buyers’ guides.
Branded vs unbranded split to manage demand capture vs demand creation.
Exports your team can drop into briefs, schema/FAQ blocks, retail media, and monthly Share-of-Model audits.
How LMO7 uses Lexem.io for growth
Seed the AI Search Audit: we benchmark your Share of Model on the exact prompts that matter.
Upgrade PDPs and A+: convert prompt paths into bullets, comparison tables, and crawlable FAQs.
Rinse and repeat: refresh clusters monthly as seasons, competitors, and models shift; track lift in assistant mentions and Amazon sell-through.
Bottom line
Lexem.io gives you a live map of buyer intent, not just a list of keywords. Start broad, localise, choose the funnel, curate the prompts, visualise the clusters, and script the paths. Then we plug it into content, PDPs, and tracking so your brand shows up everywhere it counts.
Search has shifted from exact keywords to intent. People ask assistants open questions, get reasoning-led answers, then jump to marketplaces to buy. Lexem.io maps the questions that matter for your category so your brand shows up consistently in Google, ChatGPT, and Amazon Rufus.
The Lexem.io flow
1) Start broad, then narrow
Category: pick a high-level space (e.g., Fitness).
Subcategory: choose (or type) specifics (e.g., Exercise bikes).
This “top-down” start ensures the clusters reflect the full market, not just your current copy.
2) Set geography
Global, a region, or hyper-local (e.g., UK, New York).
Localising up front captures real phrasing, regulations, and delivery constraints.
3) Add seed keywords
Paste high-converting terms from Google and Amazon.
These seeds anchor the model in proven demand while keeping room for new, adjacent intents.
4) Choose the funnel stage
Research (upper funnel awareness)
Comparison (mid-funnel evaluation)
Buying (lower-funnel purchase)
This focuses generation and clustering on the moments that matter for your goal.
5) Generate recommended prompts (dual data source)
Lexem.io blends:
ChatGPT expansion for coverage and paraphrases.
Wild-chat real queries (authentic human wording).
Together they produce a thematic map of what people actually ask, not just what keywords suggest.
6) Intent-based grouping (the clusters)
Prompts are grouped into:
Problems & solutions
Best of / top recommendations
Comparisons & reviews
General information
Buying & purchase
You can toggle branded vs unbranded views to separate category demand from brand-led demand.
7) Curate the set
Accept / reject recommended prompts.
Add your own where you see gaps or new angles.
This editorial pass locks in relevance and removes noise.
8) Save & visualise
Saved prompts form a query set.
A bubble graph shows clusters around the core theme (size by volume/priority; proximity by intent similarity).
This makes it obvious where you’re strong, thin, or conflicting.
9) Build prompt paths (how shoppers progress)
For any saved query, generate a likely follow-on sequence to mirror real behaviour.
Example (UK, Exercise bikes):
“Best exercise bike for small flats”
“Magnetic vs belt drive for noise at night”
“Watt targets for HIIT vs steady state”
“Assembly required and doorway fit UK”
“Return policy and warranty on Amazon”
These paths become briefs for content, PDP bullets/A+, FAQs, and retail-media copy.
Why clustering beats keywords (for AI search)
Intent over exact match: assistants understand paraphrases; clusters ensure coverage even when wording changes.
Constraint capture: models weigh attributes (noise, footprint, delivery) more than literal tokens.
Consistency across surfaces: one cluster set feeds Google, ChatGPT, Amazon, and your site so claims don’t conflict.
What you get out of Lexem.io
Publish-ready prompt sets by funnel stage and geography.
Cluster visualisation to guide content and PDP prioritisation.
Prompt paths to script Q&A, how-tos, comparisons, and buyers’ guides.
Branded vs unbranded split to manage demand capture vs demand creation.
Exports your team can drop into briefs, schema/FAQ blocks, retail media, and monthly Share-of-Model audits.
How LMO7 uses Lexem.io for growth
Seed the AI Search Audit: we benchmark your Share of Model on the exact prompts that matter.
Upgrade PDPs and A+: convert prompt paths into bullets, comparison tables, and crawlable FAQs.
Rinse and repeat: refresh clusters monthly as seasons, competitors, and models shift; track lift in assistant mentions and Amazon sell-through.
Bottom line
Lexem.io gives you a live map of buyer intent, not just a list of keywords. Start broad, localise, choose the funnel, curate the prompts, visualise the clusters, and script the paths. Then we plug it into content, PDPs, and tracking so your brand shows up everywhere it counts.