A query cluster is a group of semantically related questions that users ask AI engines about a shared topic, intent, or category. Rather than targeting individual keywords (as in traditional SEO), AEO strategy is built around clusters. Because AI engines generate contextual answers, similar queries produce similar responses and similar visibility outcomes.
Why clusters matter more than individual queries
In traditional SEO, you optimize a page for a specific keyword and rank (or don’t rank) for that keyword. AI engines don’t work that way; they synthesize answers for a broad range of phrasings, and your brand’s inclusion depends on how the model understands your category, not whether you’ve optimized for a specific string.
Grouping queries into clusters lets you:
- Identify which topic areas drive the most visibility for your brand
- Spot gaps where competitors appear but you don’t
- Prioritize content investment where the cluster has high query volume and low current visibility
Building a query cluster map
A query cluster for a B2B SaaS brand might look like:
Cluster: “Best CRM for startups”
- “What CRM should I use for a small startup?”
- “Best CRM for early-stage B2B sales”
- “Affordable CRM for teams under 20 people”
- “CRM that integrates with Slack”
- “Simple CRM with no setup required”
All of these queries represent the same underlying intent. A brand that appears prominently across this cluster has strong ownership of this topic in AI engines.
How to identify your query clusters
- Start with buyer questions: what do your ideal customers ask before purchasing?
- Use AI engines themselves: run your known queries and examine the related questions the AI generates
- Analyze competitor mentions: run competitor-focused queries to see which clusters they dominate
- Look at support and sales conversations: the questions customers actually ask reveal their query language
Cluster coverage as a KPI
Reading mention rate and positioning separately per cluster is what reveals the gaps a headline metric buries. A brand may have excellent visibility in one cluster (“best enterprise tool for X”) and none at all in another (“affordable alternative to Y”), and a single project-level score averages the two into something that describes neither.
This is analysis you do, not a view you open. Clustering appears in LLM Metrix only in prompt discovery, where suggested prompts are grouped by topic before you activate them; no metric is joined back to a cluster afterwards, so there is no per-cluster score, trend or filter. The practical routes are to read the per-prompt rows of a scan with your own grouping in hand, or to pull GET /api/v1/scans and aggregate them yourself.
The cheap version of the same insight costs nothing: decide your clusters before you choose tracked prompts, and keep the active set balanced across them. A prompt list that drifted entirely into one cluster will report a healthy score for a brand that is invisible everywhere else, and nothing in the number says so.