AI competitor analysis that shows who is winning recommendations, and why

Many AI visibility tools focus on presence: whether your name appears in an answer, how often it appears, and which other names appear beside it. That is a real measurement, and a business with no presence at all has a problem worth knowing about.

It is not the same measurement as preference. A citation can be a passing reference, a source footnote, or an example in a list of an industry's players, and none of those is a commercial recommendation. Share of voice tells you that you were in the room. It does not tell you why the buyer was pointed at somebody else.

That distinction is what this page is about. GetRanked AI is built to answer the next question: of the businesses an assistant could have put forward for this buyer, which ones did it actually recommend, and what did it appear to base that on.

The starting point is not a competitor list you supply. It is the answers themselves: for each buyer question, the businesses the assistants named as the ones to consider, and whether your business was one of them, one of several, or absent. That set is frequently a surprise, because the business capturing an answer is often not the rival a team has been watching, and is sometimes not a direct competitor at all.

A recommendation gap is what falls out of that. It is not a score you are missing points on; it is a specific list of the buyer questions where your business could reasonably have been put forward and was not, and the business that was put forward instead of you in each one.

Written that way, the gap has a shape. It usually clusters around a service line, a location, a type of customer or a stage of the buying decision, and the cluster is the finding. A business named confidently for one kind of question and never for another does not have a general visibility problem; it is missing the evidence that a particular kind of question needs.

For each recommendation, the report shows the reasons the assistant gave in its own words for recommending whoever it recommended. Established in the area. Clear about which services it offers. Corroborated by places the assistant treats as independent. Reviewed publicly and recently enough to matter.

These are stated reasons rather than a look inside the model, and we present them as exactly that. They are still the most useful material available, because they describe the case the assistant was able to build for one business and not for another, and that case is made of things you can change.

Assistants build answers out of what they can find and what they appear to trust, so the sources behind an answer matter as much as the wording of it. The report names the sources the assistants cited, how often each one came up, whom those sources favour, and whether your business appears in them at all.

This is usually where a recommendation gap stops being mysterious. A competitor named repeatedly across a set of questions is often present in the handful of places the assistants keep returning to for that market, and the business that is absent from those places is absent from the answers built on them.

The comparison that decides a recommendation is not who publishes more. It is what an assistant could establish about each of you: what you do, where you do it, who you do it for, and which independent sources back that up.

So each rival the answers put forward gets a dossier: what they appear to do better in the eyes of the assistants, and how you compete with them on the strength of what the answers actually said. Read next to your own reading, it turns a competitor comparison into a short list of specific differences rather than a general sense of being behind.

Answers vary. Ask an assistant the same question twice and the wording, and sometimes the businesses named, will differ. Providers also change their systems without notice, and a change on their side can move a whole market's answers in a week.

We say that plainly because it is what makes the measurement credible rather than what weakens it. A reading is a reading of a moment, taken against a fixed set of buyer questions so that two readings can be compared like for like. Movement claimed without that comparability is guesswork, and a single reading is a diagnosis rather than a trend.

Comparing gains and losses between two comparable readings belongs to the ongoing plans for that reason: it needs a second reading taken the same way as the first.

Understanding a decision is only worth the reading time if it changes what you do next. The report ends with the fixes ranked and the first three called out, each one tied to the evidence that argues for it, so the finding and the response sit together.

That is the shape of the whole thing: the question a buyer asked, the businesses put forward, the reasons given, the sources behind them, the difference between your evidence and theirs, and the first move that follows from all of it.