AI brand monitoring
Find out what assistants say about you when a buyer asks, on a schedule, with the full response and the citations kept. Then Foliora does the part a monitor cannot: it writes the page that should have been named, publishes it once you approve the exact change, re-reads the live result, and asks the same question again. From $199 per month.
Publishes through
Cited across
How it works
Monitoring and execution are usually sold as two purchases, which is why so many teams own the first and never complete the second. Here they are one loop, and the loop is the product.
Foliora generates the panel from your own catalog rather than asking you to invent prompts or pick them off a list, because the questions that matter are the ones your products and pages are actually qualified to answer. The panel is fixed and stays fixed, so month-over-month movement means something. Every answer is stored whole with its engine, date, and citation URLs.
A brand that is never named usually has no page that states the question plainly. A brand that is cited as a source but not named by the assistant has the opposite problem. Foliora reads your whole catalog within your plan's managed page count and maps each losing question to the specific page that should own it, which is most often a page you already have and already rank with.
The change arrives as a diff with the buyer question, the source, and the affected resource attached, and nothing ships until you approve that exact version. Approved work publishes through Shopify, WordPress, or Webflow, or as a pull request. Foliora then re-reads the rendered page — a successful API call is not evidence — and re-runs the questions that named a competitor, dated for comparison.
Two surfaces, one loop
Most tools collapse these into one visibility number, which hides the more actionable of the two. They have different causes and different fixes, so Foliora reports them apart.
The assistant says your name when asked who to consider. If this is low while your rankings are healthy, the usual cause is that your pages sell rather than answer: nothing on the site states the buying question in the words a person would use, so there is no passage to lift.
Your URL appears in the citations behind the answer. Being cited often and named rarely means your content is useful but your brand is not the recommendation. Being named without being cited means the model has absorbed your name without trusting a page of yours as evidence.
The connected work
Assistant answers are non-deterministic, so the design of the sampling decides whether your report is signal or noise. These are the parts that actually determine whether a number can be trusted.
Buyer questions generated from your own products, collections, and pages, so the panel measures your category rather than a guess made from your domain name.
A single answer is one draw from a distribution. Foliora samples on a consistent cadence and reports movement as directional rather than implying census-level precision.
Engine, question, market, date, the full response, and every citation URL, retained so a claim made in March can be checked in September.
The list of who was recommended in your place, question by question. This is the actionable half of the report and it is usually the part that gets summarized away.
Consequential, comparative, regulated, and newly generated claims stay behind explicit approval. Nothing reaches your domain that the business cannot support.
The rendered page re-read to confirm headings, passages, schema, and links are actually live, then the same questions re-asked and dated against the change.
What you get
Every item below is tied to a specific question and a specific page, because a rolled-up score cannot be acted on and cannot be audited.
What it costs
Most tools in this category price on how many prompts you watch. Foliora prices on how many changes get published and verified, because a buyer who wants to be more visible is buying changes rather than observations. The first snapshot costs an email address.
The full loop on one domain: research, approve, publish, verify. Add capacity when you need it.
The boundary matters
Nobody optimizes an assistant. What can be improved is the body of public evidence it reads: clearer passages on the pages that already rank, the buying question stated in plain language, evidence and dates next to the claims that depend on them, consistent organization and product entities, and structured data that makes a passage attributable. Foliora publishes those changes to your own domain, verifies them on the live page, and measures what moved.
Questions buyers ask
AI brand monitoring is the practice of asking AI assistants the questions your buyers ask and recording what comes back about you: whether you are named, whether you are recommended, how you are described, which sources the assistant cited, and which competitors appeared instead. It exists because assistants now answer the question directly rather than returning ten links, so a buyer can form a shortlist without ever loading your site. Nothing in your analytics records that conversation, which is why it has to be sampled deliberately.
Social listening watches what people publish about you. AI brand monitoring watches what a machine says about you when asked, which is a different thing with a different cause. A social mention has an author you can respond to. An assistant's answer is assembled from whatever it retrieved and whatever the model absorbed in training, so the way to change it is to change the sources it reads. That is why monitoring alone tends to be unsatisfying: the finding names a problem whose fix lives on your own website.
Yes, and ChatGPT visibility tracking is the most commonly requested version of this because it is the assistant with the most consumer usage. The mechanics are the same as for any surface: run a fixed set of buyer questions on a schedule, store the full response with the date and any citation URLs, and record which brands were named. The part people underestimate is variance. The same question can return a different set of sources on consecutive days, so a single check tells you almost nothing and a monthly comparison of the same panel tells you a great deal.
Because the panel is a sample and the score is a statistic. A small, fixed panel produces a number where a single citation gained or lost is visible: with twenty-five questions, one citation moves the score about four points, which is large enough to read as a real change. Track five hundred prompts instead and each individual citation is a fifth of a point, buried in the average, and the trend line goes smooth and meaningless. A panel that changes between months cannot produce a trend at all, however large it is.
No, and this is the most expensive misunderstanding in the category. Measuring something does not move it. A monitoring tool improves your standing only to the extent that a person reads the report and rewrites the pages, and the common outcome is a dashboard that confirms every month that a competitor is winning while nobody has the hours to respond. Before buying any tool here, count how many of last quarter's recommendations actually shipped.
It writes the fix. Foliora identifies which page on your site should own the question, drafts the change to that page — the question stated in the heading, the answer moved near the top, the supporting evidence brought onto the page, the schema that makes the passage attributable — and presents it as a diff with the buyer question, the source, and the affected resource attached. You approve or you do not. Approved work publishes through Shopify, WordPress, or Webflow, or as a GitHub pull request. Then the rendered page is re-read to confirm the change is genuinely live, and the questions that named a competitor are asked again and dated.
First establish that it is reproducible rather than one bad generation, which means asking again over several runs and keeping the responses. Then look at what was cited, because a factual error usually traces to a stale third-party page, an out-of-date directory listing, or a gap on your own site that left the model guessing. Foliora fixes the part you control: publishing a clear, dated, evidence-backed statement of the correct fact on your own domain, in a form an extractor can lift. Correcting a third-party source is outreach work Foliora does not do for you, though the record it keeps tells you exactly which page to go after.
Standalone monitoring tools in this category start around $29 per month at the low end and run into four figures for enterprise tiers, priced mostly on how many prompts you track and how many engines you track them on. Foliora is not priced that way, because monitoring is not the deliverable: Personal is $199 per month and includes three approved changes published to your site and verified each month, alongside the measured panel. Our published comparisons against several monitoring platforms name the prices we could verify and the date we read them.
No, and nobody who says otherwise controls model output either. What is checkable is narrower and more useful: here are the questions where an assistant currently names someone else, here is the page published to answer them better, here is the live URL, and here is the same question asked again afterward with the date on it. That is a verifiable claim about work performed rather than a promise about a model's behavior.
Start with the public surface
Prefer the field guide? Read the measurement method in full
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