Over time
AI brand monitoring
A reading of what AI says about a business describes one moment, and the systems producing it keep changing. Here is what moves and why, what is worth watching, and how to build a trend line you can act on today.
AI Says audits your website — the foundation every AI platform draws on. To see what ChatGPT, Gemini, Claude, Perplexity or Google AI are saying about you today, our specialists review those platforms directly during a consultation. AI Says is independent and is not connected to any AI platform. Automated, scheduled tracking is not yet available — in the meantime, re-run your free audit after every change to watch your foundations improve.
A snapshot of a moving target
Traditional search reporting got away with infrequent measurement because the thing being measured held still. A page that ranked fourth on Tuesday was very likely to rank fourth on Wednesday, so a monthly report was a reasonable description of reality.
Generative answers do not hold still in the same way. The same question, asked twice, can name different businesses — not because anything about those businesses changed, but because the system sampled differently, retrieved different sources, or is simply not deterministic. That has an awkward consequence: a single reading cannot be distinguished from an outlier. You do not know whether a 61 is your position or your best day.
This is not an argument against measuring. It is an argument for measuring more than once, and for reporting a distribution rather than a figure. A visibility rate of 40% across twenty observations is a claim you can act on. “We asked once and you were not mentioned” is not.
What a baseline is actually for
The first check earns its value in a different way: it finds the things that are unambiguously broken. A noindex tag, a robots.txt blocking everything, no structured data at all — none of those need a trend line to be worth fixing, and all of them are measured automatically. Run the free readiness check for that. The case for watching over time begins once the obvious work is done and the question becomes whether it worked.
One reading vs many
Is anything broken?
One check: Answered wellOver time: Also answeredWas this reading typical?
One check: Cannot be knownOver time: AnsweredDid my changes work?
One check: Cannot be knownOver time: AnsweredAm I gaining or losing share?
One check: Cannot be knownOver time: AnsweredDid something just change?
One check: Cannot be knownOver time: Answered
Confidence reflects the amount and quality of data available for this assessment. It is shown separately and is never folded into the AI Says Score.
Causes of movement
Five reasons your AI visibility changes without you touching anything
Understanding which cause is behind a movement is most of the value of watching it, because the four remedies are completely different.
- 01
Model updates
When a provider ships a new model version, the internal representation of your business is rebuilt from a different snapshot of the web and the answering behaviour changes with it. Businesses occasionally gain or lose mentions on the same question overnight for no reason attributable to them. This is the single largest source of unexplained movement, and it is entirely outside your control.
Remedy: none directly. Re-baseline and keep measuring.
- 02
New content — yours and everyone else's
Retrieval reflects what exists now. A competitor publishing a thorough page on a question you both want to be named for changes the selection pool for that question. So does a directory expanding its coverage of your category, or an industry publication running a round-up.
Remedy: publish against the questions you are losing, not in general.
- 03
Competitor moves
Entity work by a competitor — schema, profile consistency, corroborating listings — raises their confidence score as an entity and makes them a safer business for a system to name. Competitive share can fall while nothing about you deteriorates at all.
Remedy: match the entity hygiene, then differentiate on proof.
- 04
Changing citations
The set of sources a system reaches for shifts as pages are re-crawled, re-indexed, restructured or retired. A page that cited you last month may have been rebuilt without the mention. Your own URL changes are a frequent and self-inflicted version of this.
Remedy: keep citable URLs stable; monitor which sources replace yours.
- 05
Your own site, whether or not you noticed
A theme update that strips structured data, a staging rule that leaves a noindex in production, an SSL or redirect change that breaks crawling, a CMS migration that changes every URL. Technical regressions are common, silent, and they affect the signals that are fully within your control.
Remedy: re-run the readiness audit after every deployment.
Scope
What is worth tracking over time
Six things worth watching over time. Five are what a specialist reviews on the platforms for you today; the sixth — your own site’s readiness — you can track yourself with the free audit.
Score history
The overall score and every component, plotted over time with the observation count behind each reading so a move can be judged against the confidence it carries.
Question-level presence
A stable question library re-run on a fixed cadence, so presence and absence are comparable between runs rather than compared across different prompts.
Competitive share trend
Who is named alongside you and how that mix shifts. Share is the metric where a trend is genuinely more meaningful than any single value.
Citation changes
Which sources are cited for your category, when your own domain enters or leaves that set, and which third-party page replaced you when it does.
Accuracy drift
Whether claims made about your business stay true as descriptions are regenerated. Inaccuracies tend to appear quietly and persist until somebody checks.
Readiness regressions
The thirty-two site checks re-audited, so a deployment that silently removes your structured data is caught quickly. This is the one item on this list you can already do yourself, free, whenever you like — re-run the audit after a deployment.
In development
What is on the roadmap
The direction of travel for automated tracking. These are plans rather than products you can buy today; until they launch, a specialist does this work with you.
| Tier | Intended scope | Status |
|---|---|---|
| Automated platform trackingAutomated, recurring checks of what AI platforms say about you, tracked over time. Not built yet — today this is specialist work done during a consultation. |
| Not yet available |
Meanwhile
How to build your own trend line now
You can assemble a useful history yourself, starting today. Three habits do most of the work, and a specialist can handle the platform side for you.
- 1
Fix the cadence
Re-run the readiness audit on the same day each month. Comparability comes from holding the inputs still, so keep your confirmed business details the same between runs.
- 2
Record the context, not just the score
Note what you deployed, what you published and what you noticed competitors doing between runs. A score history with no change log cannot attribute anything.
- 3
Re-audit after every deployment
Site signals respond immediately and regress silently. The readiness audit is the one part of this you can verify the same afternoon.
Start from a free check and keep the results. Platform-specific behaviour is covered on the ChatGPT, Gemini and Perplexity pages.
Questions
Questions about monitoring
Track the two layers separately: re-run your free AI Readiness audit after every website change, and have a specialist review the platforms themselves in a consultation. The audit shows your foundations moving straight away, and the specialist review shows how ChatGPT, Gemini, Claude, Perplexity and Google AI are describing you and your competitors. Automated, scheduled platform tracking is not yet available, so this pairing is the most reliable way to build a trend line now.
A single check is a snapshot, and generative answers vary from one asking to the next. Ask the same question twice and the businesses named can differ, so one reading establishes a baseline and flags anything obviously broken. Knowing whether a result was typical, whether you are improving, or whether a change had any effect needs repeated readings over time.
Monthly suits most businesses, using a stable set of questions, with an extra check after any substantial change to your website. That cadence is frequent enough to separate a real change from ordinary variation, and spaced enough that the readings are not dominated by noise. Daily readings of a non-deterministic system mostly measure the system.
Act on sustained changes rather than single readings. Signals worth attention include disappearing from a high-intent question across consecutive checks, a new competitor appearing consistently, a factual claim about you changing, or your own domain dropping out of the cited sources. A one-off fluctuation is rarely news.
Your readiness score updates immediately — re-run the audit after a deployment and the result reflects the change at once. AI answers follow more slowly and less predictably, because they depend on re-crawling, re-indexing and sometimes on a model being updated. That lag is why a trend across several readings tells you more than any individual one.
Yes — competitive share is one of the most useful things to watch, because it only becomes meaningful as a trend. A competitor appearing once in an answer is an observation; a competitor appearing in most answers to your highest-intent questions for three months running is a commercial problem worth acting on. A specialist can research this for you in a consultation today.
Set your baseline today
Check My AI Readiness, keep the result, and you have the first point on your own trend line.