About
Why TrackGeo exists
TrackGeo measures how AI assistants answer the questions your buyers actually ask — which of them mention you, who gets recommended instead, and which sources the model read to decide.
This page is not a founding story. There is a more useful thing to put here: the thesis the product is built on, and the rules we have written into it to keep our own numbers honest.
A channel that does not keep receipts
Search was legible. Someone typed a query, saw a list of links, clicked one, and arrived carrying a referrer that said where they had been. Every step left a record, and an entire discipline grew up around reading those records well.
AI-assisted discovery is not legible in the same way. A buyer describes a problem in their own words. A model reads sources it selected, composes an answer, and names two or three products. The buyer may not click anything at all. If they arrive later, they often arrive by name or direct, with nothing attached to say what shaped the decision. The step that built the shortlist is the step nobody logged.
That gap is the whole reason this product exists. You cannot manage a channel you cannot see, and you cannot see this one with analytics designed for clicks. The only honest way to observe it is to ask the models the same buyer questions, repeatedly, and record exactly what they say — the answer text, the products named, the sources cited, and how much that varies between runs.
That is a narrower promise than most of this category makes. It is also one we can keep.
The rules we hold our own numbers to
These are not values on a wall. Each one is enforced in the product, and each one costs us something — a shorter engine list, an emptier dashboard, a more careful claim.
We query the models, not the apps
TrackGeo does not scrape a chat window. It asks provider APIs the same buyer questions your customers ask, on a schedule, and records what comes back. That is repeatable and defensible. It is also not the consumer surface, which is why we say which surface it is every time we show a number.
We name the surface, not the brand you expect
One engine we support is Microsoft's Azure-hosted OpenAI deployment, so we call it Azure OpenAI. Another is Gemini answering with live Search grounding, which approximates the shape of a search-grounded AI answer without being one. Printing the more famous name in either slot would be better marketing and worse measurement.
An engine ships when its adapter does
An engine appears on this site only when a production adapter exists and real prompt runs exercise it. We have left well-known assistants off the list for exactly that reason. A longer logo wall would be easy to build and would be selling a label rather than a measurement.
Observed, estimated and inferred are three different words
Most AI engines strip referrers, so attribution here is genuinely hard. TrackGeo separates traffic it directly observed from influence it estimated and baselines it inferred, and labels each. Blending the three into one confident number is the fastest way to make a dashboard look better and a decision get worse.
An empty state stays empty
When a workspace has no real mentions yet, TrackGeo shows nothing rather than something plausible. A seeded figure reads as data the workspace has not earned, and anyone who acts on it has been misled by us rather than by a model.
Every finding carries its evidence
Scores are arguments, not verdicts. Each one opens into the runs behind it, the sources the engine cited, and how confident we are given how much we sampled. A number that cannot survive being clicked on does not belong on the screen.
TrackGeo measures answers from provider APIs — the models behind these assistants — not scrapes of their consumer apps.
What we will not tell you
A measurement product is defined as much by its caveats as by its charts. These are the ones that matter most.
That an answer is the answer
Model outputs vary between runs. A single response is an anecdote. We sample repeatedly and report the pattern with a confidence level, and we would rather show a low-confidence label than a clean number that is not earned.
That we watched your buyer
We query provider APIs, not consumer apps. What we measure is how the underlying model answers your buyers' questions, which is a strong proxy and not a recording of anyone's session.
That every visit can be attributed
Most AI engines strip referrers. Some AI traffic is directly observable and a lot of it is not, so we separate what we saw from what we estimated and label both.
That other people's results predict yours
We have no customer logos on this site and no borrowed statistics. Your category, your competitors and your sources decide your numbers.
All of it is written up properly on the methodology page, including how sampling works and what each score does and does not mean.
Part of the Tracklink Marketing Intelligence Suite
TrackGeo is one product in a suite. Its job in that picture is AI-assisted discovery — how assistants describe your brand, which buyer questions you show up in, and which sources decide those answers.
TrackGeo
AI visibility and pipeline intelligence. You are here.
Fair questions about us
Is TrackGeo related to TrackLink?
Yes. TrackGeo is part of the Tracklink Marketing Intelligence Suite, alongside TrackLink and NeuralEye. TrackGeo covers AI-assisted discovery specifically: how assistants describe your brand, which buyer questions you appear in, who they recommend instead of you, and which sources shape those answers.
What does TrackGeo actually query?
Provider APIs — the models behind the assistants — rather than scrapes of their consumer apps. We ask those models the same buyer questions repeatedly and record the answers, the products they name and the sources they cite. Results will differ from what you see in a chat window, and we would rather explain that than imply we are reading over your buyer's shoulder.
Why are some well-known assistants missing from your engine list?
Because we only list engines we can genuinely query through a production adapter that real prompt runs exercise. Naming an assistant we cannot reach would be selling a label rather than a measurement. When an adapter ships, the engine appears here; if one is removed, it disappears.
Do you publish customer names, logos or case studies?
Not yet. Every example on this site is labelled as illustrative, and no figure in our marketing is presented as a customer result. When there are results worth publishing, they will be published with the method attached, because a number without its method is decoration.
How can I check any of this?
Read the methodology page. It covers how prompts are sampled, how scores are computed, what confidence levels mean, and what the numbers should not be read as. If something there does not match what the product shows you, that is a bug worth telling us about.
Judge it on your own category
The fastest way to test any of this is to point it at your brand and see whether the findings survive scrutiny.
Or read the methodology, the engines we query, the feature set, or the blog.