AI SEO · AI Visibility Tracking
AI Visibility Tracking
that shows what actually moved.
Every other part of AI SEO changes something. This is the part that tells you whether the models now name you, whether they cite your domain, and whether what they say about you is true.
“Which agency should I use for e-commerce SEO in Poland?”
- ChatGPT 62% named
- Perplexity 54% named
- Google AI Overviews 31% named
The answer engines we monitor on your behalf
Our AI visibility strategy
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01
Freeze the prompt set
The same questions every month, because a moving question set measures nothing.
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02
Sample, never snapshot
Repeat runs per engine, since model output varies on its own between calls.
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03
Score five things
Presence, citation, competitive share, characterisation and factual accuracy.
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04
Tie change to cause
Every movement mapped back to the specific work that preceded it.
AI visibility tracking is the practice of measuring how often AI answer engines name your brand and cite your domain, by running a fixed set of buyer questions against ChatGPT, Gemini, Claude, Perplexity, Copilot and Google AI Overviews on a schedule and recording what each one says.
There is no Search Console for answer engines
Google hands you impressions, positions and clicks for free. An answer engine hands your buyer a paragraph and hands you nothing. If you want a number, someone has to go and collect it.
There is no ranked list
A rank tracker works because results arrive in order, at an address it can request. An answer engine writes prose. Nothing in it holds a position, so the thing being measured has to be presence rather than rank.
Analytics only sees the survivors
The conversation happens inside the assistant. Your referral data records the handful of people who clicked through afterwards, which means it undercounts every occasion your brand was discussed and no link was followed.
The output moves on its own
Ask the same question twice and the wording changes. Models are probabilistic, personalisation shifts what a given account sees, and vendors ship new versions without notice. One reading proves very little.
What a visibility report actually contains
Because the source is noisy, the honest unit of reporting is a sampled rate with its range attached, read as a trend line across weeks of runs. Anyone selling you a single confident percentage is rounding away the part that matters.
| Metric | What it answers | How we measure it |
|---|---|---|
| Answer presence rate | Across the prompts we run, how often does the engine mention your brand at all? | We count the runs in which your brand name appears in the answer text, reported per engine and per prompt group. |
| Citation share | When the engine attaches sources to its answer, how often is your domain one of them? | We capture the source list on every run and record whether your domain appears, alongside the domains that displaced it. |
| Competitive share of voice | Of all the brands the engine names in your category, how much of the naming belongs to you? | We log every brand named beside you on each run and split the total across that named set. |
| Characterisation | When a model describes you, what does it actually say, and how does that read to a buyer? | We store the sentences written about you run by run and classify their framing and sentiment. |
| Factual accuracy | Is what the model states about your services, markets and credentials still true? | We check named claims against your own record and flag anything wrong or out of date for correction. |
Who this is for
Tracking earns its keep wherever a decision is riding on the answer, and wherever somebody is about to be asked what the last two quarters of work achieved.
Teams already paying for AI SEO
If there is an invoice for Answer Engine Optimization, entity work or schema, tracking is what turns it into a result you can show. A baseline taken before the work starts is the only thing that lets you attribute anything afterwards.
Brands losing traffic without losing rankings
Your organic SEO reporting shows positions holding and impressions steady while sessions drift down. Tracking tells you whether answer engines are absorbing those queries, and which questions went first.
Categories with an incumbent
One rival keeps coming back in every answer and everybody in the room senses it. Share of voice replaces that sense with a measured gap, and shows month by month whether the gap is closing or widening.
Companies the models describe badly
An engine confuses you with a similarly named firm, quotes a price you retired, or omits a service line. Accuracy monitoring catches those claims while they are still cheap to correct at the source.
How we build the measurement
The method is deliberately boring, because a measurement is only worth reading if it was taken the same way every time.
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1
Build the prompt set
We write out the real questions your buyers ask, branded and unbranded, grouped by intent. That set is then frozen, because a prompt set that keeps changing cannot be compared with itself.
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2
Take the baseline
A full run across every engine before anything ships. Whatever comes back is the reference point that every later report is read against.
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3
Fix the conditions
Same cadence, same market settings, same language, repeated samples per prompt. Region and personalisation change answers, so those variables get pinned down and reported per market rather than averaged away.
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4
Record presence and citations
Every run is captured in structured form: the answer text, the brands named in it, the sources linked beneath it, and the model version where the vendor exposes one.
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5
Aggregate into trend lines
Individual runs are noise. We roll them into rates with ranges, so you can see whether a move sits outside normal variation before anyone acts on it.
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6
Annotate and attribute
The timeline is marked with what shipped and when a vendor announced a release. That is what lets a change in the line be credited to your work or ruled out as a model update.
What you actually receive
Data you keep and a method you can audit. Everything below is handed over in a form your team can rerun without us.
The prompt set and its baseline
- Every buyer question we track, written out
- Which engine names you today, and which names a rival
- The opening reading each metric is measured against
The monthly visibility report
- Answer presence rate per engine and per prompt group
- Citation share for your domain, with the sources cited instead
- Sample counts and ranges, so the noise is visible
The competitor board
- Every brand named alongside you, counted per engine
- Share of voice split across the named set
- The questions a rival currently owns outright
The accuracy and characterisation log
- What each engine says about you, stored run by run
- Wrong or outdated claims flagged with the source behind them
- A correction queue ordered by how often the claim recurs
“We started with a free strategy that showed us specific gaps in our campaigns. Today, LineUp is our regular partner, delivering a measurable return on every invested zloty.”
What clients say on Google
Unedited screenshots from our Google Business Profile. Swipe, or use the arrows, to read them all.
Read these reviews on Google. Reviews written in Polish or Turkish are shown in Google’s own translation.
Questions we get asked
What is AI visibility tracking?
AI visibility tracking is the practice of measuring how often AI answer engines name your brand and cite your domain. A fixed set of buyer questions is run against each engine on a schedule, and every answer is recorded so presence and citation rates can be compared over time.
Why does a rank tracker not work for this?
A rank tracker reads an ordered list of results from a page it can request. An answer engine returns written prose with no positions in it, so there is nothing to scrape a rank from. Presence and citation have to be measured directly from the answer text.
The answers change every time we ask. Does that make tracking pointless?
No, it makes single readings pointless. Model output is probabilistic, so we sample the same prompt repeatedly and report a rate with its range rather than one result. Movement is judged across weeks of runs, which is how any noisy measurement is handled.
Do personalisation and location affect what you record?
Yes. Engines adapt to account history, region and language, so a run from Warsaw can differ from a run from London. We fix those variables per tracked market and hold them constant, then report each market separately instead of blending them into one figure.
What happens when a vendor ships a new model version?
Results can shift for reasons that have nothing to do with your site. We log the version where a vendor exposes it and annotate the timeline when a release is announced, so a step change in the data can be attributed to the model rather than to your work.
Can we buy tracking without the optimisation work?
Yes, and plenty of clients start there. Tracking on its own tells you where you stand and which questions are worth fighting for. Acting on it is Answer Engine Optimization, and the two are billed separately so the measurement stays honest.
Find out where you stand before you change anything.
We will run your real prompt set across every major engine and send back the baseline: which questions name you, which name a competitor, and how each engine describes your business today.
Last reviewed 28 July 2026.