How many queries do I need in my bank to start?

Start with 50-100 queries covering your core brand, category, and comparison queries. This gives you enough data to establish meaningful Share of Model baselines. Expand to 200+ over the first quarter as you identify gaps and new opportunities.

Should I use the same queries for all AI engines?

Yes, use the same core query bank across all engines. This enables apples-to-apples comparison of your visibility across ChatGPT, Perplexity, Gemini, Claude, and others. You can add engine-specific queries for engines with unique behaviours, but the core bank should be consistent.

How often should I run my query bank against AI engines?

Weekly monitoring is the recommended cadence for active optimisation. This balances data freshness with resource efficiency. During intensive optimisation sprints or around major AI model updates, increase to daily monitoring for your highest-priority queries.

Resource

Template

Query Bank Builder Template

Build, organise, and maintain the query bank that drives your AI visibility monitoring and strategy.

Quick answer

Your query bank is the foundation of AI visibility measurement. It defines which questions you track across AI engines and directly determines the accuracy of your Share of Model, citation rate, and competitive intelligence data. A poorly constructed query bank gives you misleading data; a well-built one reveals actionable opportunities.

Overview

About this template

This template provides a structured approach to building a query bank from scratch, including category frameworks, priority scoring, and maintenance workflows. It covers commercial queries, informational queries, comparison queries, and brand-specific queries — each requiring different monitoring approaches.

The template scales from a starter bank of 50 queries to an enterprise bank of 500+. Use it alongside AEO Platform to systematically track AI engine responses and measure the impact of your optimisation efforts.

Query Categories

1
Brand queries: Direct brand and product name mentions
"What is [Brand]?" "Is [Brand] good?" "[Brand] vs [Competitor]" — queries where your brand is explicitly named.
2
Category queries: Generic category and solution queries
"Best [category] software" "Top [solution] tools" — queries where AI engines choose which brands to recommend.
3
Comparison queries: Head-to-head and multi-brand comparisons
"[Brand A] vs [Brand B]" "Compare [products]" — queries that trigger direct competitive matchups.
4
Problem queries: Pain-point and solution-seeking queries
"How to solve [problem]" "Best way to [achieve goal]" — queries where AI engines recommend solutions.
5
Informational queries: Educational and research queries
"What is [concept]?" "How does [technology] work?" — queries that establish thought leadership and expertise.

Query Selection Criteria

1
Business relevance: Does this query relate to your product or service?
Prioritise queries that, if answered with your brand, would drive meaningful business outcomes.
2
Search volume: Is this query asked frequently enough to matter?
Use traditional search volume data as a proxy — high-volume search queries are also asked to AI engines.
3
Competitive opportunity: Do competitors appear where you do not?
Target queries where competitors have visibility and you are absent — these represent the clearest wins.
4
Citation potential: Can your content realistically be cited for this query?
Ensure you have (or can create) content that directly and authoritatively answers the query.
5
Conversion proximity: How close is this query to a purchase decision?
Balance monitoring between top-of-funnel awareness queries and bottom-of-funnel decision queries.
6
Engine coverage: Which AI engines are most relevant for this query type?
Some query types perform differently across engines — monitor where your audience actually searches.

Priority Scoring Framework

1
Assign each query a priority score from 1-5
5 = critical business query, 1 = nice-to-know informational query. Use this to allocate monitoring resources.
2
Weight by revenue impact potential
Queries that drive high-value conversions should be scored higher regardless of volume.
3
Factor in competitive intensity
Queries where multiple competitors already appear are both higher-opportunity and higher-effort.
4
Adjust scores quarterly based on performance data
As you gather citation data, reprioritise queries based on actual visibility trends.

Maintenance Workflow

1
Review query bank monthly for additions and removals
Markets shift, products launch, competitors emerge — your query bank must evolve accordingly.
2
Add queries when launching new products or features
Every new offering should have 5-10 associated queries added to your monitoring bank.
3
Remove queries that are no longer business-relevant
Discontinued products, old campaigns, and irrelevant categories should be cleaned out.
4
Track query bank coverage against your total addressable market
Ensure your query bank represents the full scope of queries your brand should appear in.
5
Share query bank updates with content and product teams
The query bank should inform content strategy — new queries highlight content gaps to fill.

FAQ