Every SaaS brand lives or dies on five query archetypes in AI engines: category queries, comparison queries, use-case queries, competitor queries, and brand-direct queries. Each one requires a different fix.
AI engine visibility is not uniform across all the queries related to your brand. Analysis of citation patterns across 500 SaaS brands shows that visibility concentrates around five query archetypes — and most teams are only optimising for one of them.
The five query archetypes
Category queries ("best project management software", "alternatives to Jira"): These are the highest-volume, lowest-specificity queries. AI engines answer them with ranked lists. To appear here, you need: (1) Wikipedia or Wikidata entity presence, (2) mentions in high-CC-frequency comparison articles, (3) structured data with Category or applicationCategory. Most brands appear in 1-2 of the four engines on category queries.
Comparison queries ("Notion vs Asana", "ClickUp alternatives"): Engines answer these with head-to-head summaries. To appear: be mentioned in existing comparison content indexed by Common Crawl, or create your own comparison content and get it cited. Reddit and G2/Capterra reviews are the primary source material for AI comparison answers. See the Reddit monitoring playbook for the full approach.
Use-case queries ("project management software for remote teams", "task tracker for agencies"): These are mid-specificity, high-intent queries. Engines answer with recommendations for the specific context. To appear: create use-case-specific pages with HowTo or FAQPage schema targeting each context, and distribute them through Reddit communities serving that audience.
Competitor queries ("ChatGPT alternatives", "Slack competitors"): Users searching for competitor alternatives are high-intent. Engines include brands that are reliably compared to the named competitor in training data. To appear here, your brand needs to be co-mentioned with the competitor across multiple high-frequency sources. Press and earned media is the fastest path.
Brand-direct queries ("what is Notion", "how does Linear work"): For these, the engine typically pulls from Wikipedia, the brand's own website, and Wikidata. Brands without Wikipedia presence often get a thin or inaccurate answer here. Fix: Wikipedia presence strategy and Wikidata completeness.
How to prioritise
The query archetypes have different leverage points:
| Query type | Primary lever | Time to impact |
|---|---|---|
| Category | Wikidata + Wikipedia | 3-6 months |
| Comparison | Reddit + G2 coverage | 4-8 weeks |
| Use-case | Content + FAQPage schema | 2-6 weeks |
| Competitor | Co-mention in press | 6-12 weeks |
| Brand-direct | Wikipedia presence | 2-4 months |
Most teams default to creating more content (use-case and comparison). The highest-leverage gap for most brands is category queries — where Wikipedia and Wikidata presence is the deciding factor, not content volume.
What this means for your roadmap
- Run the five query types for your brand in Perplexity, ChatGPT, and Claude
- Note where you appear, where you don't, and which competitors appear where you don't
- Map each gap to the primary lever from the table above
This is exactly what the Veezow scan surfaces automatically — citation position by query type, with prioritised fixes ordered by leverage. Run a free scan to see where your brand stands across all five archetypes.
Put this into practice
See how your domain scores on the signals covered in this edition. Veezow runs a free AI visibility scan — robots, sitemap, structured data, bot access, and off-site presence.
Run a free scan →