The best tool depends on what you need to measure. Ahrefs Brand Radar is the broadest search-backed option documented here; Peec AI is strongest for segmenting visibility and sentiment; Profound adds citation and prompt-volume analysis. The other candidates can fit narrower workflows, but several require current vendor verification before you commit.
Every platform in this guide samples a defined set of prompts, sends those prompts to one or more AI systems, and records the resulting answers. That reveals how your brand appears under tracked conditions—not every private conversation in ChatGPT or another model. Because answers can change between runs, useful monitoring requires repeat observations, consistent prompts, and clear metric definitions.
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What a ChatGPT mention tracker actually measures
A mention tracker is a monitoring system for answer-level visibility. You supply a brand, competitors, markets, and either a prepared prompt library or your own questions. The service runs those questions against supported AI engines and stores the answers. It then identifies whether your brand appeared, where it appeared, how it was described, which competitors were named, and which sources the answer cited.
That is different from analytics that only show visits from AI systems or requests from AI crawlers. Referral reports tell you that someone arrived from an AI surface; crawler logs show that a bot fetched a page. Neither proves that an answer recommended your company, described it accurately, or cited your content. A mention tracker works at the answer and prompt level.
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The metrics are not interchangeable
- Mention: whether the brand name appears in an answer.
- Position: where the brand appears in a list or recommendation, when the tool can determine an order.
- Sentiment or perception: whether the wording is favorable, neutral, negative, or factually concerning according to the platform’s method.
- Citation: which pages or domains the answer links to or names as sources.
- Share of voice: the brand’s share of tracked answers or mentions relative to competitors.
- Impressions or prompt volume: an estimate of how often tracked questions are searched or represented in the index; it is not a count of real ChatGPT conversations.
- Referral traffic: visits recorded in your web analytics after an AI referral. This is downstream behavior, not answer visibility.
Choose tools and dashboards according to the outcome you need. A public-relations team may care about factual accuracy and sentiment, an SEO team about citations and source pages, and a product team about position in buying recommendations.
Quick comparison of the nine candidates
| Tool | Best fit | Documented capabilities or angle | Pricing evidence |
|---|---|---|---|
| Ahrefs Brand Radar | Broad discovery plus custom monitoring | Search-backed index, custom prompts, historical responses, mentions, citations, share of voice, competitor comparisons | $199/month per index or $699/month for all platforms; custom-prompt tiers of $50, $100 and $250/month are listed in 2026 help documentation. USD prices may vary by region. |
| Peec AI | Segmentation and brand perception | Visibility, position and sentiment filters by model, country and prompt tags such as persona or funnel stage | Current price not stated in the cited material. |
| Profound | Answer-engine and citation analytics | Answer-engine visibility, sources cited across answers and prompt-volume insights | Current price not stated in the cited material. |
| Otterly.AI | Candidate for recurring monitoring | Included in a June 2026 comparison of relevant tools; exact engines, limits and terms need confirmation from its current product documentation | Current price and limits require verification. |
| Semrush AI Visibility Toolkit | Teams already using the Semrush ecosystem | Listed as an AI-visibility candidate in a secondary comparison; current package scope needs confirmation | Current price and package limits require verification. |
| Waikay | Fact accuracy and positioning checks | A 2026 roundup describes a focus on AI-generated fact accuracy and brand positioning | Current price and feature scope require primary-source verification. |
| xofu | Bottom-of-funnel questions | A 2026 roundup characterizes it as focused on bottom-of-funnel prompts | Current price and coverage require verification. |
| Morningscore ChatGPT Tracker | Small teams wanting a starter trial | A 2026 roundup lists a free trial and paid plan; current terms may have changed | Trial and plan details require verification. |
| RankScale | SMB-oriented AI SEO workflow | A 2026 comparison characterizes it as an SMB-focused AI SEO toolkit | Current availability, limits and price require verification. |
The first three have the clearest vendor-documented capabilities in the available material. The remaining six are useful shortlists, not independently tested winners. Product pages, supported engines, regional coverage and commercial terms can change, so verify them during procurement.
1. Ahrefs Brand Radar: the broadest documented coverage
Brand Radar combines two approaches that solve different problems. Its search-backed index is designed for discovery at scale, while configurable prompts let you monitor the exact questions your customers ask. The 2026 Ahrefs help documentation describes more than 405 million search-backed prompts across seven AI platforms and historical responses from 2025. A separate product page has shown a larger 475-million figure; treat those as different page or coverage versions rather than combining them.
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Cadence and pricing
Ahrefs documents monthly refreshes for its chatbot indexes. Custom prompts can be scheduled from monthly to daily, so a reputation-sensitive query can be checked more often than a broad discovery index. The help documentation lists $199 per month for a single-platform index and $699 per month for all platforms. It also lists custom-prompt tiers at $50, $100 and $250 per month with different included check counts. These are USD list prices from documentation accessed in 2026 and may vary by region or change before purchase.
How to use it well
- Start with the index to find recurring questions and competitor patterns.
- Move your highest-value buying, comparison and support questions into custom prompts.
- Set a consistent schedule and annotate major model or product changes.
- Review citations separately from mentions: a favorable answer without a source you control may still leave a credibility gap.
Ahrefs says it “extracts People Also Ask questions from keyword queries in its keyword index, then enters those questions into the web version of each AI chatbot using the default model.” That method explains why index results are useful for search-derived discovery but do not represent every conversational prompt.
2. Peec AI: segment visibility by market and intent
Peec AI is the clearest fit when one overall visibility score is too coarse. Its vendor description covers visibility, position and sentiment tracking, with filters for model, country and prompt tags. Tags can represent a persona or funnel stage, allowing a team to separate, for example, enterprise procurement questions from consumer research.
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Questions Peec can help answer
- Does the brand appear more often in one model than another?
- Is its position stronger in one country?
- Do bottom-of-funnel prompts produce more favorable sentiment than awareness prompts?
- Which competitor gains visibility when the prompt is reframed for a different persona?
Before choosing Peec, define the taxonomy you will use. Country, model and intent filters are only useful when prompts are tagged consistently. Ask for current engine coverage, sampling limits, export options, retention and price during evaluation; those details are not established in the available documentation.
3. Profound: connect answers, citations and prompt demand
Profound presents a broader answer-engine analytics model. Its official site describes answer-engine visibility, the sources cited across answers and prompt-volume insights. This combination suits organizations that need to connect three questions: how often the brand is present, which evidence the model relies on, and how much demand surrounds the tracked prompt set.
Use citation reporting to find pages that repeatedly influence answers, then inspect whether those pages are accurate, current and aligned with your positioning. Prompt-volume insights can help prioritize work, but treat them as estimates tied to the provider’s data rather than as a census of AI usage. The site’s customer outcome figures are vendor-reported case-study claims, not independent benchmarks.
4. Otterly.AI: verify the recurring-monitoring details
Otterly.AI appears in a June 2026 comparison of five relevant products, making it a reasonable candidate for a recurring subscription workflow. The available material does not establish its current supported engines, prompt limits, refresh schedule, export formats or price. Do not select it on a feature checklist copied from an old comparison.
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- Which answer engines and model versions are included in the plan you would buy?
- Can prompts be grouped by country, language, persona and funnel stage?
- Does the service preserve the full answer and cited URLs for audit purposes?
- How are reruns, failed calls and model changes represented in trend charts?
Those answers determine whether Otterly.AI is suitable for an agency managing several brands or only for a small exploratory project.
5. Semrush AI Visibility Toolkit: a possible ecosystem fit
A secondary comparison lists the Semrush AI Visibility Toolkit among relevant candidates. That makes it worth investigating for teams already operating inside the Semrush workflow, where shared projects, permissions and reporting could reduce tool sprawl. The available product page did not provide usable current details for package limits, supported engines or pricing.
Request a live demonstration using your own prompt set. Confirm whether the toolkit records answer text and citations or only aggregates a score, whether data can be segmented by market, and how often each engine is refreshed. Existing subscription status should not substitute for checking those capabilities.
6. Waikay: investigate the factual-accuracy angle
Ahrefs’ 2026 roundup describes Waikay as focused on AI-generated fact accuracy and brand positioning. That angle is valuable for companies whose main risk is a wrong product specification, outdated policy or misleading description rather than a missing mention.
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Treat the description as a discovery lead until current primary documentation confirms the workflow. Ask how facts are defined, whether findings are tied to the exact prompt and model response, how corrections are prioritized, and whether the system distinguishes a factual error from an unfavorable opinion. Do not assume that a general visibility score provides the same protection as a fact-audit process.
7. xofu: focus monitoring on buying-stage prompts
The same roundup characterizes xofu as focused on bottom-of-funnel prompts. A narrow, purchase-oriented tracker can be useful when your immediate question is whether AI systems recommend your brand for a specific job, category or comparison.
Build a test set around real buying language: “best,” “alternative,” “for a team of,” “with integration,” and “under a stated constraint.” Then check whether xofu lets you preserve the exact wording, market and model for each run. Current coverage, limits and price still need verification, so compare its output with a general-purpose tracker before treating a change as a market trend.
8. Morningscore ChatGPT Tracker: a provisional starter option
Ahrefs’ roundup lists a free trial and a paid plan for Morningscore ChatGPT Tracker. Those terms may be stale, but the product can be considered by a small team that wants to learn the mechanics of prompt monitoring before purchasing a larger platform.
During a trial, test whether you can add your own prompts, rerun them on a schedule, inspect the complete answer, identify citations and export results. Confirm which model or web experience is queried: “ChatGPT” can refer to different interfaces and model defaults. A trial that reports only a simple presence score may not meet an agency’s reporting needs.
9. RankScale: an SMB-oriented AI SEO shortlist
A 2026 comparison characterizes RankScale as an SMB-oriented AI SEO toolkit. That positioning may suit a lean marketing team that wants AI visibility alongside a broader SEO workflow rather than a dedicated enterprise answer-intelligence system.
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Because current vendor documentation was not established, verify the fundamentals before relying on it: supported engines, prompt count, country and language controls, historical retention, citation capture, competitor comparisons and user permissions. If the product emphasizes a composite score, ensure you can still inspect prompt-level evidence; otherwise, diagnosing a visibility change will be difficult.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to compare tools before you buy
1. Check engine and regional coverage
List the exact surfaces your audience uses: the web version of ChatGPT, other chatbots, AI search experiences or regional variants. Ask whether the tool names the model and version, records the country and language, and keeps those conditions stable between runs. “Seven platforms” or “multiple engines” is not enough if your priority market is missing.
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Prebuilt, search-derived indexes provide breadth and faster discovery. They are useful for finding common questions you did not think to write yourself. Custom prompts provide relevance: you can monitor the precise questions asked by sales prospects, support agents or procurement teams. Strong programs use both, rather than treating one as a replacement for the other.
3. Demand prompt-level evidence
A score without the underlying answer is hard to audit. Look for the full response, timestamp, engine, prompt, detected brand position, sentiment rationale where available, and every cited source. This record lets you distinguish a true change from a parser error or a different answer variant.
4. Compare cadence with volatility
Monthly index refreshes may be adequate for broad strategy. A product launch, crisis or fast-changing policy may justify daily custom checks. More frequent polling is not automatically better: it can create noisy charts and higher costs when the underlying answers vary from run to run. Establish a baseline and use repeated observations before declaring a trend.
5. Test segmentation and reporting
For teams with multiple markets or audiences, verify filters for model, country, language, persona, funnel stage and competitor. Agencies should also test project separation, client exports, scheduled reports, API access and permissions. A tool that has the right metric but cannot deliver evidence to stakeholders may create manual work that outweighs its subscription.
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6. Price the complete monitoring plan
Calculate the number of brands, engines, prompts and reruns you need each month. Include overage rules, historical retention, seats, exports and regional taxes. A low entry price can become expensive when every additional market or daily check consumes a separate allowance. Recheck all prices on the vendor’s current page; the documented Ahrefs figures are examples of dated list prices, not a promise that they remain unchanged.
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A practical setup for reliable tracking
- Write a measurement brief. Define whether the goal is awareness, recommendation, factual accuracy, citations, competitive share or referral impact. Assign one primary metric and supporting metrics.
- Build three prompt groups. Use brand questions, category and competitor questions, and high-intent buying questions. Keep the exact wording and record the market, language and intended persona.
- Establish a baseline. Run each prompt enough times to see normal variation before changing content or making a public claim. Save the answer text and citations, not just a score.
- Separate discovery from monitoring. Use a search-backed index to uncover common questions, then schedule custom prompts for the questions that affect revenue or reputation.
- Audit citations. Check whether cited pages are yours, accurate, current and representative. A citation from an outdated page can create a factual risk even when the brand is visible.
- Annotate changes. Mark product releases, major content updates, model changes and campaign launches on your trend chart. This prevents unrelated events from being interpreted as proof of causation.
- Report examples with aggregate metrics. Show the percentage of tracked answers containing the brand, position distribution, sentiment or accuracy findings, competitor presence and top cited sources. Include representative prompt-level evidence for decisions.
Common mistakes and how to avoid them
- Calling a prompt sample “market share.” It is share of voice within your tracked questions unless the provider defines a broader, defensible universe.
- Treating one answer as a trend. AI outputs vary between runs. Repeat the same conditions and look for persistent movement.
- Mixing engines without labeling them. A result from one model or interface cannot be assumed to describe another.
- Confusing mentions with citations. A model can name a company without linking to its site, or cite a page without recommending the company.
- Using referral traffic as a visibility proxy. Many AI answers produce no click, and analytics cannot reveal brands that were mentioned but did not receive a visit.
- Ignoring prompt wording. “Best accounting software” and “best accounting software for a two-person nonprofit” can produce entirely different competitors and positions.
- Assuming vendor case studies are benchmarks. Treat customer outcome figures published by a vendor as case-study statements, not independent market measurements.
- Buying before verifying availability. Several candidates in this list come from secondary roundups. Confirm current engines, limits, terms and data retention directly.
Which tool should you choose?
Choose Ahrefs Brand Radar when you need broad, search-derived discovery plus a documented custom-prompt path and can budget for its indexed platforms or checks. Choose Peec AI when segmentation by model, country, persona or funnel stage is central to the decision. Choose Profound when citation sources and prompt-volume context belong in the same answer-engine report.
Evaluate Otterly.AI or the Semrush toolkit when workflow integration matters, but verify current product details first. Investigate Waikay for fact-accuracy work, xofu for buying-stage prompts, Morningscore for a possible starter trial, and RankScale for an SMB-oriented SEO workflow—while treating each as provisional until its current documentation matches your requirements.
No evidence here establishes one universal winner. The defensible choice is the platform that tracks the engines your audience uses, preserves the prompts and answers you need to audit, refreshes at a useful cadence, supports your segmentation, and fits the number of brands and checks you will actually run.
Frequently Asked Questions
Can these tools monitor every ChatGPT conversation about my brand?
No. They run defined prompts under specified conditions and analyze the returned answers. Private conversations and untracked prompts are outside their measurement universe.
How many prompts should a new monitoring program start with?
Start with a small, documented set covering brand, category, competitor and high-intent buying questions. Expand after you understand normal answer variation and which prompts influence decisions.
Should I track citations even if my main goal is brand awareness?
Yes, if credibility or accuracy matters. A mention without a useful, current source can leave a misleading answer uncorrected; citations also show which pages influence model responses.
How should agencies preserve client evidence?
Store the exact prompt, answer, engine or model label, country, timestamp and cited URLs for each reported change. Confirm that the selected platform supports exports, permissions and historical retention.
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What should I do when two runs produce different answers?
Keep the conditions constant, rerun on the schedule you selected, and report distributions or persistent patterns rather than treating either single response as definitive.
Are AI crawler logs a substitute for mention tracking?
No. Crawler logs show that an automated agent fetched a page, while mention tracking shows what an AI answer said, which brands appeared and which sources it cited.
Quick Recap
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