GetMentions AI earns attention because it keeps the answer text and cited URLs behind the score, then lets you act on the gaps it finds. I think it is the best current fit I can defend for teams that want repeatable measurement and a managed execution layer in the same workflow.
The verdict up front
GetMentions AI makes the most sense when your reporting problem starts after the first chart: you need to rerun a stable prompt panel, see the exact answer and the pages behind it, and turn a gap into ordered work.
You should trial it if your team keeps hitting the same wall: the report says you lost visibility, but nobody can show which source changed or what to do next.
● Best for: SEO, content, and digital PR teams that need source-level diagnosis and one system for the handoff from measurement to execution
● Why I would trial it: the workflow carries you from saved answers and citation gaps into a real request queue
● Main limitation: standard plans include four of the six platforms, with all six as an add-on
● Pass for now if: you need the wider model set without Enterprise, or you only want official single-platform reporting
What kind of product this is
GetMentions AI sits in the AI visibility category, but it behaves more like an operating system for the work around that category. The core pieces are prompt-based tracking, citation intelligence, brand-gap discovery, and managed placements for brands and agencies.
That scope fits the way AI answers are being assembled. When Reddit expanded its partnership with Google in 2024, it described giving Google structured access to Reddit's public posts and comments, both to display that content across Google products and to train models. If your reporting ignores community threads and cited videos, you can miss a meaningful part of why a brand appears or disappears.
Setup
Setup starts with the inputs you would expect: brand name, domain, country, language, and optional aliases. Check the domain twice. Once you confirm it, it cannot be changed.
Fill in the aliases. If buyers call your brand by a product name, an abbreviation, or a legacy name, add it before the first run.
From there, the product reads the site and proposes topics, buyer personas, competitors, and a starter prompt set. On HubSpot it proposed eight topics and 52 competitors in one pass. Edit this step hardest. The topics mirror how your own site describes the market, so if your site language is too broad, your first panel will be too.
What the HubSpot run showed
One run is a snapshot, not a trend, but it shows what the product does with real answers. Across the 96 answers, HubSpot appeared in 38.5% and led the visibility ranking, ahead of Salesforce at 30.2%. The platforms disagreed more than that average suggests:
|
Platform |
How often HubSpot appeared |
|---|---|
|
Google AI Mode |
54% |
|
ChatGPT |
46% |
|
Gemini |
33% |
|
Perplexity |
21% |
The source layer was sharper still. Only 179 of the 489 pages cited across those answers mentioned HubSpot. Six Reddit threads were cited, and none of them named HubSpot. The insertions queue turned that gap into five articles that already get cited and leave HubSpot out, each with a suggested way in.
That is the product in miniature: a score, the pages behind it, and a short list of places to act.
Building the prompt panel
Prompt selection is where weak AI visibility reporting usually breaks. Two teams can ask two different sets of buyer questions and tell two different stories with equal confidence.
You can generate prompts from the site and personas, import your own prompt CSV, group prompts by topic and funnel stage, and decide whether prompts should mention your brand, competitor names, or both.
Split by funnel stage early. A top-of-funnel question and a bottom-of-funnel comparison do not measure the same job, and a blended report hides that.
Balance branded and comparison prompts. Too many branded prompts flatter your visibility; too many head-to-head questions make the market look harsher than it is. The product puts that choice in front of you.
Choose platforms before prompts. That is where the standard-plan limit becomes a buying decision, and a disciplined panel on the right surfaces beats a bigger one on the wrong ones.
Inspecting saved answers
The saved-answer view is the screen I kept coming back to. GetMentions AI stores the answer text and the cited URLs behind each run, so when a stakeholder asks why your brand vanished from a question, you can open the actual response instead of arguing from a percentage alone.
That changes the meeting.
You can see whether the brand appeared, where it appeared inside the answer, and which cited pages were shaping the response. Of the summary metrics, watch average rank first. A mention buried at the bottom of a list and one near the top are different outcomes.
The overview filters do a lot of work here: platform, funnel stage, market, topic, persona, tag, and date range. One blended number becomes something a team can act on.
The prompt tab also splits prompts into those where the brand is mentioned and those where it is absent. Use that missing list to plan the week. It is more actionable than another chart about overall movement.
Reading citation gaps
The citation layer is where the product stops feeling abstract. Citation Intelligence shows cited domains and cited URLs, classifies each source by domain type and page type, and shows whether your brand or competitors appear on that source.
If you have ever had to answer, "Which page is feeding this answer?" this is the screen that earns its place.
I found the URL view more useful than the domain view once the question turned from reporting to action. A domain can tell you which publisher or source class matters. A URL tells you which exact listicle, review page, Reddit thread, or article you need to inspect.
Use it to decide whether a missing mention is a sitewide problem or a page-level one.
In the brand-mention workflow, each opportunity is marked as in-network (shown on screen as In Inventory), outreach-based, or not influenceable. Those three labels are enough to separate immediate tasks from pages you should simply monitor. If a page is not influenceable, it is context, not a task queue.
This is also where the dedicated UGC and YouTube views justify themselves. If Reddit threads, Quora answers, forums, or YouTube channels shape your category, you want them as their own work surfaces, not buried in one long citations export. The UGC view is diagnostic only: it shows where community threads shape answers, but it does not post or run outreach there.
Where tracking turns into managed work
GetMentions AI becomes more distinctive when you move from a missing citation to the next step. The product does not leave you with a gap list and a CSV.
You get three routes out of that list:
● Insertions for existing pages that are already being cited and could potentially add your brand
● Placements for new editorial content on external sites
● YouTube mentions for creator and channel placements tied to cited video sources
The order flow runs end to end. Request pricing, approve the offer, and follow the order through to a live URL. You pay for approved work by card or from a prepaid wallet balance that rolls over and does not expire.
The separate AI Ads Tracker identifies advertisers showing up in ChatGPT answers and flags prompts where paid presence exists while your brand is absent organically. It is a useful diagnostic for paid and organic teams. Coverage is ChatGPT-only today.
How much you should trust the numbers
The evidence chain is straightforward: a fixed prompt set, the stored answer text, and the cited URLs behind every metric. If somebody challenges the report, you have something concrete to open.
You still need to be disciplined about interpretation. Language models do not reliably give the same answer twice, even with every setting fixed for consistency: researchers who tested five models this way across eight common tasks found that none delivered repeatable accuracy on every task. A GetMentions AI study of 530,875 AI citations across four engines found that most of the sources behind a typical answer change from one day to the next. One before-and-after check is weak evidence. A fixed panel and repeated reruns are much more persuasive.
The answer text itself also needs caution. In NewsGuard's audit of the 10 leading AI chatbots in 2025, the tools repeated false claims on controversial news topics 35% of the time, almost double the 18% rate of a year earlier. A citation list tells you which pages were shown. It does not tell you the answer got them right, which is why the stored answer text matters.
Source transparency matters more because most people will not do the checking themselves. In a University of Melbourne and KPMG study of more than 48,000 people in 47 countries, 66% said they rely on AI output at work without evaluating its accuracy. If your team cannot open the answer text and the source list behind a reported movement, you are asking stakeholders to trust a black box.
GetMentions AI handles that part well. It does not solve the category's volatility. No tool does. It gives you a clearer trail when you need to explain what happened.
Team access and exports
Every plan includes unlimited team members. Admins handle billing and the team, Editors can change prompts and markets and place orders, and Viewers get view-only access, which suits clients. Everyone who needs the data can see it without seats turning into a budgeting argument.
Charts leave the platform as images or CSVs with a prior-period comparison, and filtered reporting data exports as CSV. That covers report decks, handoffs to other teams, and audit trails outside the product.
Pricing and buying model
As of 2026, the public GetMentions AI pricing and coverage rules answer most of the buying math up front. Plans differ by prompts, brands, markets, refresh cadence, and platform coverage. Every plan includes every feature.
Standard plans include any 4 of these 6 platforms by default: ChatGPT, Gemini, Google AI Mode, Google AI Overviews, Microsoft Copilot, and Perplexity. Any standard plan can be extended to all 6 as an add-on. Enterprise adds seven more models by API: Claude, Grok, Llama, Mistral, Qwen, DeepSeek, and Kimi.
|
Plan |
Prompts |
Brands |
Markets |
Weekly refresh |
Daily refresh |
|---|---|---|---|---|---|
|
Starter |
50 |
1 |
1 |
$25/mo |
$85/mo |
|
Growth |
100 |
2 |
3 |
$45/mo |
$155/mo |
|
Pro |
200 |
3 |
5 |
$80/mo |
$289/mo |
|
Scale |
350 |
5 |
10 |
$135/mo |
$479/mo |
|
Enterprise |
custom |
custom |
custom |
custom |
custom |
The trial terms are clear too: 7 days, no credit card, full platform access, and up to 25 prompts per run. Use it on one brand and one market. That is enough for a disciplined evaluation, not for a sprawling multi-market test.
The cadence math is better than the sticker shock suggests. Starter daily is $85 a month against $25 for Starter weekly, but it refreshes every day instead of once a week, so each data point costs about half as much. Start weekly if your prompt panel is stable. Pay for daily if answer movement matters; it is the cheaper way to buy each read.
Annual billing gives one month free.
What worked in testing
● The reporting layer is inspectable. You can open the answer, read the citations, and work backward from the movement instead of defending a score by itself.
● Setup gives you a structured draft of the market, then lets you edit it before the first run.
● The path from citation gap to ordered work is real. Once a source matters, you can move it into insertions, placements, or YouTube requests without rebuilding the job somewhere else.
What would give me pause
● Standard plans cover four of the six standard platforms, and the extra model set is Enterprise-only.
● The AI Ads Tracker is limited to ChatGPT today.
● Placement and insertion pricing is quoted inside the workflow rather than published as a menu.
So, is it the best AI visibility tool right now?
For teams that need measurement and execution in one place, I think the answer is yes. The HubSpot run shows why in a single pass: where the brand stood on each platform, the 310 cited pages that left it out, and five articles to act on, all inside one workflow.
In the trial, open three screens before you judge it: a saved answer, to see whether it explains why you moved; citations by URL, to see whether it separates real opportunities from dead ends; and the insertions list, to see whether the request flow is something your team would keep using.
FAQ
Do standard plans include all six AI platforms?
No. Standard plans include any 4 of ChatGPT, Gemini, Google AI Mode, Google AI Overviews, Microsoft Copilot, and Perplexity by default. If you need all 6, there is an add-on.
Do I need Enterprise for Claude or Grok?
Yes. Claude, Grok, and the rest of the extra model set are added by API on Enterprise, so scope Enterprise from the start if you report on them.
Does GetMentions AI go beyond monitoring?
Yes. It moves from prompt tracking and citation gaps into managed work: existing-page insertions, editorial placements, and cited YouTube channels.
How much does GetMentions AI cost?
Plans start at $25 a month for Starter with a weekly refresh, or $85 a month with a daily one. There is a 7-day trial with no credit card, and annual billing gives one month free.
