vector search: what it means
Finding results by semantic similarity rather than keyword match. Here is how it shows up in AI answers and what to do about it.
Vector search is finding results by semantic similarity rather than keyword match. It sounds narrow until you see it decide which pages an AI engine quotes. This entry covers the definition, a concrete example and how to measure it.
vector search is finding results by semantic similarity rather than keyword match. In practice, it is the difference between being mentioned in an answer and being the source the answer relies on.
Why it matters for AI visibility
AI answers are assembled from retrieved sources. Anything that affects retrieval — structure, markup, entity clarity, freshness — changes whether vector search works in your favour. Teams that ignore it tend to optimise the visible page and lose the invisible retrieval step.
A concrete example
A buyer asks an engine for the best tool in your category. The engine retrieves five pages, quotes three and cites two. If your page is one of the five but not one of the two, you have a vector search problem, not a traffic problem.
How to measure it
| What to check | Where | What good looks like |
|---|---|---|
| Appearance in the answer | Prompt view | You appear in 25–40% of target prompts |
| Citation to your URL | Citation graph | At least one owned URL per answer set |
| Accuracy of the claim | Manual review | No factual errors on branded prompts |
| Freshness | Page date | Updated within 90 days |
Related terms
Frequently asked questions
What is vector search in one sentence?
How do I improve vector search?
Does it affect Google as well as AI?
Methodology and sources
- Citekit product and pricing data, 9 November 2026.
- Vendor pricing pages and product documentation, checked November 2026. Verify current pricing with each vendor before purchase.
- Public documentation for schema.org, llms.txt and the AI engines named on this page.
How Citekit compares
Citekit is measured against Profound, Scrunch AI, Otterly.AI and nine other tools in the category. The table is the short version; every comparison has a full, sourced page.
| Tool | Entry price | Engines | Best fit |
|---|---|---|---|
| Profound | from $499/mo | 4 | Enterprise teams with a dedicated analyst |
| Scrunch AI | from $250/mo | 5 | Mid-market content teams |
| Otterly.AI | from $29/mo | 4 | Small teams testing AI visibility |
Full comparisons: Profound · Scrunch AI · Otterly.AI · all 12 competitors
Citekit Research. “vector search: definition and AI visibility impact.” Citekit, updated 29 August 2025. https://citekit.apps.antelligen.com/glossary/vector-search