GEO Audits for the AI search era. Page by page, query by query.
GEO URL Audits analyses any page on your site and measures how well its content covers the full range of queries an AI search system would generate around its topic. You get a semantic coverage score, a query-by-query breakdown, and clear guidance on where the gaps are.
Content optimization in the age of AI retrieval.
Traditional search engines index keywords. AI search systems work differently. When a model handles a query about your product, category, or brand, it doesn't look for exact matches. Instead, it generates a web of related sub-questions and evaluates whether the available content can answer them.
A page optimised for a single keyword can score well in traditional SEO and still fail to appear in AI-generated responses, because it only addresses one angle of a topic that an AI model approaches from ten.
GEO URL Audits replicates that process. It takes your URL, identifies the core entity, generates the full set of semantically related queries an AI search system would derive, and measures whether your content actually covers them.
The result is a coverage score and a query-level map that shows you precisely where your content stands, and what it would take to improve it.
How it works
Step 01 · Submit your URL
Enter any URL from your site. GEO URL Audits identifies the primary entity or topic of the page and extracts the relevant content chunks it will evaluate against.
Step 02 · Query fan-out
The tool generates a set of synthetic sub-queries derived from the page's core topic, simulating the internal search behaviour of an AI system exploring that subject from multiple angles.
Step 03 · Semantic coverage analysis
Each sub-query is matched against the page's content using semantic similarity. The tool identifies which queries are fully covered, which are partially addressed, and which represent genuine gaps.
Step 04 · Coverage score and report
You receive an overall coverage score, a query-by-query breakdown with Full Coverage, Partial Coverage, or Gap status for each, and the best-matching content fragment. Everything exportable as CSV.
Step 05 · Optimise and re-audit
Once you've acted on the recommendations, re-run the audit on the same URL to measure the improvement. The re-audit uses the same methodology, so the before-and-after comparison is clean and consistent.
What the audit tells you
Four outputs per audit. Structured, actionable, exportable.
Coverage score
The percentage of AI-generated queries your page answers sufficiently. High = retrieval-ready. Low = systematic gaps.
Coverage score
Queries generated
The full set of sub-queries the tool derived from your page topic. Angles your strategy may not have considered.
Queries generated
Covered vs. Partial vs. Gaps
Query-level breakdown. Covered = strong match. Partial = needs work. Gaps = highest-priority targets.
Covered vs. Partial vs. Gaps
Best matching fragment
For each query, the closest content match. Shows whether gaps are missing topics or underdeveloped ones.
Best matching fragment
Query: "La Bomba perfume notes"
"...a heady blend of ylang ylang, jasmine, and cedarwood with notes of vanilla and black pepper..."
“In the old world, you could rank by being the loudest voice.
In the AI world, you rank by being the most useful.”
Traditional search engines have been moving away from "just keywords" for a decade, but AI-powered engines have accelerated this shift into a completely different league: the leap from Lexical Search to Semantic Search and RAG (Retrieval-Augmented Generation).
The core difference lies in how the system interprets intent. When a user asks an AI engine a complex question, the AI internally generates a web of sub-questions to fill its "knowledge gaps".
If your page is "thin" meaning it repeats the target keyword but lacks the depth to answer the "why" and "how" behind a user's broader query, the LLM will recognize that the content won't help it satisfy the user's intent. It will skip your page in favor of a source that provides real information density.
To win in AI search, content strategy must evolve from keyword-centric to entity-centric. You aren't just trying to rank; you're trying to be the definitive semantic source that AI models require to verify a claim.
Andreas Niessen
Co-Founder, Prominence AI
Know which pages are ready for AI search. Fix the ones that aren't.
GEO URL Audits runs on a monthly credit model designed for strategic use. Growth includes 10 audits per month, Scale includes 50. Custom volumes available for intensive workflows.
The credit model is not a constraint. It is an incentive to prioritize. Audit the pages that matter most to your AI visibility strategy first.
Frequently Asked Questions
Everything you need to know about GEO URL Audits and semantic coverage analysis.