Map the competitors that AI engines are most ready to cite.

When to map competitor AI visibility

Map competitor AI visibility when a single audit isn't enough to make a decision. A standalone score tells you where you stand; it doesn't tell you whether a 72 is leading your category or trailing it. Run a multi-competitor analysis when you're entering a crowded space, planning a content investment, or trying to explain to a stakeholder why a rival keeps showing up in ChatGPT and Perplexity answers and you don't.

The analysis is most useful in three moments: before you commit budget to GEO work and want a baseline against the field; after a competitor's traffic or share of AI mentions shifts and you need to know what changed technically; and during a quarterly review, where a repeated run shows whether your relative position improved or the category as a whole moved.

What a competitor GEO gap reveals

A gap is the difference between your category breakdown and a rival's, read against the scoring weights. Because every domain is graded on the same 100-point scale across 8 categories, a gap points to a specific, fixable cause rather than a vague "they rank better" feeling. The categories carry uneven weight, so where the gap sits matters more than how big it looks:

• robots.txt (18) and llms.txt (18) — an access or discovery gap here is the most expensive. If a competitor lets AI crawlers in and you don't, no amount of content closes the distance.
• Schema (16) and meta (14) — a competitor with valid structured data and clean canonical or Open Graph tags is easier for an engine to parse and attribute.
• Content (12) and brand & entity (10) — multi-page topical coverage and consistent entity signals are where a thin site falls behind a deep one.
• Signals (6) and AI discovery (6) — language declaration, freshness, and the AI discovery files are smaller but cheap to fix once spotted.

A gap concentrated in the high-weight categories is a priority; a gap spread across the 6-point categories is housekeeping. Reading the gap by weight is what turns a comparison into a plan.

A worked example: one SaaS against three rivals

A project-management SaaS keeps seeing one of its competitors cited when prospects ask an AI engine "what's the best tool for remote teams". It runs a multi-competitor analysis on its own domain plus three rivals. The breakdown is more informative than any single number:

• All four sites have a strong meta and content profile — that's table stakes in the category, not a differentiator.
• The two most-cited rivals both publish an llms.txt and valid FAQ and Organization schema. The SaaS publishes neither.
• One rival blocks AI crawlers in robots.txt and scores lowest overall, confirming the cost of an access gap even with good content.

The conclusion writes itself: the SaaS isn't losing on content, it's losing on the high-weight technical signals (llms.txt and schema) that the cited rivals already have. That's a two-week fix, not a quarter-long content project — and the analysis is what made the priority obvious. To confirm the change later, the team re-runs the analysis against the same three domains and watches the gap close.

Single audit vs head-to-head vs multi-competitor

The three views answer different questions. Pick the smallest one that answers yours:

• Single audit — your own baseline, one domain, full category breakdown. Start here to know where you stand before you look at anyone else. Run a free GEO audit.
• Head-to-head compare — two domains, side by side, for when one rival matters most and you want a focused diff. Compare two sites.
• Multi-competitor analysis — your domain plus several rivals in one run, for reading a whole category and finding where you sit in the field. That's this page.

How to run a competitor analysis

1. Enter your URL and competitor URLs. Add your own domain and the competitor domains you want to benchmark — the rivals that already surface in AI answers for your category.
2. Run the batch audit. Each domain is scored across the same 8 GEO signal categories on a 100-point scale, so the results are directly comparable.
3. Read which domain AI engines are most ready to cite. Compare the scores and category breakdowns to see which site has the fewest blockers and the strongest citable signals for AI answer engines.
4. Prioritise fixes by scoring weight. Close the gaps that carry the most points first — robots.txt and llms.txt are worth 18 points each — then re-run the analysis to confirm the gap narrowed.

Earning citations is a function of the signals AI retrieval rewards — open access for AI crawlers, structured data, an llms.txt, quotable statistics, and multi-page topical coverage. The guide to appearing in ChatGPT and Perplexity and the entity authority guide cover the playbook behind the categories you're benchmarking.

LLM competitor audit: what it can and cannot measure

An LLM competitor audit is this analysis named after the engines instead of the method. The distinction worth keeping is between the model and the layer it reads. ChatGPT, Perplexity, Claude and Gemini answer product questions by retrieving from the live web first, so what decides whether you are quoted is the retrievable layer: whether the crawler is allowed in, whether an llms.txt and valid schema describe you, whether a passage is quotable on its own.

That layer is public, which is why a competitor can be audited at all — the audit reads only what any AI crawler can fetch. What cannot be audited from the outside is the model itself: nobody can inspect its weights, or prove why one brand surfaced and another didn't. So an honest LLM competitor audit measures readiness to be cited across domains, not citation counts. If you need to know who is actually named for a given question, that is a separate measurement — a citation check against a live answer engine — and the two are complementary: readiness explains the outcome the citation check observes.

Competitor adjacency: who you are named next to

Adjacency is how often you and a rival appear in the same AI answer, and in which order. It is a different question from visibility, and a more uncomfortable one. Being absent from a category answer is a problem you can see; being present but consistently named third, after the same two competitors, is a problem that looks like success in a dashboard.

Adjacency is largely offsite. It is produced by the third-party pages an engine cites when it composes an answer — comparison articles, review directories, forum threads, "best tool for X" listicles. If those sources name your rivals and not you, the engine inherits that framing regardless of how clean your own markup is. This is the ceiling of any on-site audit, and it is worth stating plainly: closing every gap on this page makes you retrievable, but the sources that mention you decide the company you keep.

Practically, the two work in sequence. Use this analysis to remove the technical reasons an engine would skip you, then look at which domains are cited in the answers for your category — the citation checker shows the sources behind a live answer — and treat those as the offsite surfaces where adjacency is won or lost.

Frequently asked questions