How Do You Track LLM Mentions? 9 AI Brand Monitoring Platforms

You track LLM mentions by running a fixed prompt set - questions a real buyer would ask - across several models on a schedule, then recording where your brand appeared, where it didn’t, and what the model said when it did. LLM brand monitoring is the practice of sampling how generative models describe, rank and omit your brand in AI answers, alongside the social, news, review and web mentions your existing monitoring already covers.

Start with questions buyers ask before they know your brand name. A prompt that already names your brand tells you how an assistant describes it. An unbranded prompt tells you whether it comes up at all.

Quick answer

• Traditional monitoring plus AI: Brand24 - Individual $199/month on annual billing, plus the $99/month AI module: approximately $298/month before tax.
• Low-cost AI-only test: LLM Scout - $39.99/month Standard, with ChatGPT-only monitoring.
• Dedicated AI visibility: Chatbeat - $99/month with a no-card trial; related to Brand24’s AI visibility technology.
• Existing PR workflow: CisionOne - AI Visibility Dashboard announced July 14, 2026; quote-only pricing.
• Existing enterprise social workflow: Hootsuite LLM Insights through enterprise listening; confirm entitlements in the quote.

The 9 platforms at a glance

For each plan, check which engines it tracks, how often it runs your prompts and whether you can export the raw answers. Check if AI monitoring costs extra when it’s sold alongside social listening.

Platform	Best for	Standout feature	Starting price	Free plan / trial
Brand24	Traditional mentions plus AI	Social, web, news and AI visibility module	About $298/month: $199 annual-billed base + $99 AI	14-day base trial; confirm AI access
Lumen by Talkwalker / Hootsuite	Enterprise social teams	LLM Insights in enterprise listening	Quote only	Demo
CisionOne	PR and communications	Trajaan-powered AI visibility in media intelligence	Quote only	Demo
SE Ranking	SEO teams	UI-based answer collection plus SEO reporting	€63.20/month annual add-on view; base plan extra	Trial; confirm allowance
Semrush	SEO and AI visibility	Brand Performance and Prompt Tracking	$99/month standalone, 25 prompts	No standalone toolkit trial
Peec AI	AI search analytics	Prompt, source and competitor reporting	Starter $95/month; confirm billing term	Confirm current trial
Chatbeat	Dedicated AI monitoring	Related to Brand24 AI technology	$99/month	Trial, no card
GetMint	Multi-market brands	GEO workflow and EU hosting	€99 monthly or €74/month annual Starter view	Confirm current trial
LLM Scout	Small AI-only experiments	Vendor-published cross-model study	$39.99/month, ChatGPT-only	Seven-day trial

The 9 platforms, reviewed

Brand24

Brand24 combines traditional mention monitoring with an optional AI visibility module. The Individual base plan starts at $199/month billed annually. Budget for the AI module separately. Brand24 pricing

The AI visibility module adds $99/month, bringing the entry combination to approximately $298/month before tax. It provides 30 prompts across four AI surfaces. Confirm whether each engine, export and trial entitlement is included in the offer.

Brand24’s social, news, web and review coverage serves a different collection task from generated-answer monitoring. Bundling them can simplify workflow, but does not remove the need to validate sampling and entity matching. Extra keywords, mention allowances and other processing features can increase the total.

Lumen by Talkwalker (Hootsuite)

Lumen by Talkwalker links enterprise social listening with LLM visibility analysis. Hootsuite’s LLM Insights describes visibility, position, mentions, competitor share of voice and sentiment dimensions.

Access requires enterprise listening entitlements rather than assuming that the self-serve Hootsuite plans include the same LLM capabilities. Talkwalker pricing is by quote. Ask which engines, prompt counts, history and exports the contract covers.

Integration may suit an established communications team, but marketing claims about consumer adoption or setup speed are not a measurement-accuracy benchmark.

CisionOne

CisionOne’s AI Visibility Dashboard connects AI answer monitoring with media intelligence. Cision announced the dashboard on July 14, 2026, powered by Trajaan. The announcement lists ChatGPT, Gemini, Claude, Perplexity, Grok, DeepSeek, Google AI Overviews, AI Mode and Mistral.

The announced features include share of voice, sentiment, prompt and topic analysis, and a list of publications and domains cited in sampled answers. Ask which features are live and included in your quote.

Cision’s organization and Fortune 500 adoption figures are vendor-reported. Its claim that native integration avoids fragmented dashboards expresses product positioning rather than proof that it outperforms specialist monitors. Pricing is by sales quote.

SE Ranking

SE Ranking offers AI visibility within an SEO workflow. Distinguish its ChatGPT-specific tracker from the wider AI Search Toolkit, which covers multiple AI search surfaces and may include sentiment features depending on the product and tier.

SE Ranking describes direct UI-based collection of answers. This means collecting observed interface responses rather than inventing synthetic answers; it is still sampling across prompts, runs, times, sessions and locales. Cached answers help audit the resulting metrics but do not turn them into a census.

The reviewed euro pricing view lists the AI Search add-on from €63.20/month billed annually, plus a required base plan. Verify prompt allowances, engines, API/MCP access and trial limits for the chosen configuration. Do not compare an add-on price with another vendor’s complete subscription.

Semrush

Semrush’s standalone AI Visibility Toolkit costs $99/month and includes 25 tracked prompts. Its documentation distinguishes analysis and research query allowances from scheduled prompt tracking. Semrush One bundles include 50, 100 or 200 tracked prompts depending on plan.

Brand Performance includes sentiment-related analysis and location/language reporting. Check report-specific coverage and collection method rather than assuming all reports have identical engine coverage.

The standalone toolkit has no free trial. Additional domains and sub-user licenses cost extra, and annual account billing can affect subscription alignment. Compare the full configuration with specialist products; existing Semrush ownership is a workflow benefit, not a requirement for the economics to work.

Peec AI

Peec AI is a specialist AI search analytics platform, not social media publishing or scheduling software. Its monitoring covers prompt answers, brand visibility, competitors and cited sources.

Published pricing material lists Starter $95/month for 50 prompts, Pro $245 for 150 and Advanced $495 for 350. All three include three models. Advanced increases prompt capacity; it is not automatically the solution to wanting more engines. Confirm billing terms, extra model charges and current trial access.

Peec can sit beside a social or media monitoring product, but it should not be presented as providing those traditional channels itself.

Chatbeat

Chatbeat is a dedicated AI visibility product at $99/month with a no-card trial. It tracks brand presence, competitors and source/citation patterns across supported AI surfaces.

If you only need AI answers, price Chatbeat on its own. If you also need social or web monitoring, compare it with Brand24’s base plan plus the AI module. Brand24’s Chatbeat documentation · Chatbeat pricing

Confirm engines, prompt allowances, refresh frequency and exports during the trial. Case-study gains are vendor-reported outcomes, not guaranteed traffic improvements.

GetMint

GetMint offers AI visibility and GEO workflows for brands monitoring multiple markets. Its Starter pricing shows €99/month on monthly billing or €74/month equivalent on annual billing in the reviewed euro view. Currency toggles and billing terms must be kept distinct.

Starter lists two AI models, 50 prompts, two topics, five competitors and one country with daily refresh. Growth has larger allowances; confirm its current billing configuration before quoting a monthly equivalent. EU hosting and content optimization are part of its positioning, but confirm residency and contractual requirements directly.

It is AI-focused, so a separate product may still be needed for social, news and reviews. A trial is not evidence of a permanent free monitoring tier.

LLM Scout

LLM Scout offers a low-cost single-model pilot and publishes tables from its own cross-model research. Those summary tables are not an open raw-response dataset.

Standard is $39.99/month for one brand, 25 prompts and ChatGPT only. Advanced is $99.99/month for 100 prompts across ChatGPT, Perplexity and Claude. There is a 7-day free trial.

LLM Scout’s Frank Vitetta analysed 26,996 responses to 1,552 unique prompts logged over nine months. His October 5, 2026 study includes outcome tables and 95% confidence intervals. It omits the underlying data, brand names and prompts, and had no outside review. Vitetta’s analysis · LLM Scout research release

Why your mention count is an estimate, not a census

A mention rate describes the responses collected, not every answer that buyers receive. Sampling uncertainty, session conditions and prompt selection matter. Confidence intervals require assumptions about the design and dependence among observations; repeated collection alone does not supply valid intervals.

LLM Scout’s vendor-authored study reports the following outcomes for 749 product-selection prompts with responses from all four assistants:

Outcome for the tracked brand	Prompts	Share
None of the four named it	402	53.7%
All four named it	143	19.1%
Exactly one named it	117	15.6%
Exactly two named it	45	6.0%
Exactly three named it	42	5.6%
Mixed mention/omission outcome	204	27.2%

On 27.2% of prompts, some assistants mentioned the brand and others omitted it. Of the 347 prompts with at least one mention, all four assistants mentioned the brand on 143, or 41.2%.

In the 185-prompt subset containing “best” or “top”, 64 prompts had mentions from all four assistants: 34.6% all-four coverage. The remaining 121, or 65.4%, had at least one omission. That is not a 65.4% chance of being absent from every answer or from any particular buyer’s answer.

The study also reports different individual-assistant mention rates, but their prompt mixes differ, preventing a like-for-like ranking. The underlying data is not public and the work was not independently reviewed. These descriptive results support checking cross-model variation, not universal probabilities for other brands.

Track competitors using the same measurement frame

Share of voice provides context but is not inherently more stable than mention rate. It remains sensitive to prompt selection, model variation, the competitor set and changes in competitors’ mentions. Compare brands on the same prompts and runs, document denominator changes, and preserve a core benchmark with a separate exploration set.

An omission where a competitor appears is a lead for investigation, not automatic proof of a content gap. Inspect raw answers and sources, repeat suspicious results and assess whether the change persists. Document prompt and competitor revisions and establish a new baseline when necessary.

False positives: when your brand name is also a common word

Brands such as Apple, Sage and Signal can be confused with ordinary words or other entities. Generated answers may also use aliases or describe a product without its exact name. Monitoring needs both matching and contextual validation.

Test raw results during onboarding, including known mentions, omissions, aliases and ambiguous contexts. Review false positives and false negatives separately. Use output-level entity filters or exclusions when supported; changing a buyer prompt just to force an unambiguous mention can bias the visibility benchmark.

Keep relevant prompts stable, document matching-rule changes and check examples after product or model updates. A noisy demo is a reason to investigate, not proof that paid data must be noisier.

Sentiment on AI answers needs its own validation

AI answers can contain praise, criticism, descriptive limitations and conditional recommendations. Position and omission are separate outcomes, not sentiment labels. Traditional social sentiment classifiers also face ambiguity, sarcasm and domain-specific language; neither task should be treated as universally solved.

Semrush Brand Performance, relevant SE Ranking products and CisionOne advertise sentiment-related features. Feature availability is not evidence of accuracy. Request label definitions, validation methodology and examples relevant to your brand, rather than claiming no vendor publishes evidence.

Review a representative sample of positive, negative and neutral labels against human judgments. Measure agreement and inspect false positives, including factual descriptions such as a limited free plan. Use unvalidated labels as review flags. There is no universal need to wait two quarters before using sentiment responsibly.

How LLM monitoring slots into the stack you already have

LLM monitoring can complement existing social, news and review monitoring, but it needs different quality controls. Traditional monitoring finds existing documents; AI monitoring solicits generated responses under a defined sampling frame. Costs include collection, infrastructure and reporting, not simply inference or storage.

Three practical options are a dedicated tool alongside the current stack, Brand24 with its paid AI module, or enterprise integration through CisionOne or Lumen/Hootsuite. Confirm exports, API entitlements, identifiers and retention so the reports can be joined. Native integration does not establish superior accuracy.

Sprout Social remains an adjacent traditional-channel candidate. AI-assisted social features alone do not establish consumer LLM answer monitoring. Confirm any current LLM-monitoring offer before including it in a comparison of those capabilities.

How to run a useful monitoring programme

1. Build buyer-intent prompts from customer questions, with discovery, comparison and use-case segments. Choose a manageable pilot size rather than assuming one required count.
2. Select the assistants your buyers use. A single-model pilot can answer a narrow question; broader conclusions require broader coverage. Four engines are not a universal minimum.
3. Choose daily or weekly collection based on the decisions, budget and repeat-run design. Daily runs can improve observation density; weekly runs can reduce cost. Neither cadence guarantees statistical reliability.
4. Record absences, citations, prompt versions, locales and model changes alongside mentions. Preserve a core comparison set and document revisions.
5. Set alerts using observed variability and business impact. Investigate sustained changes and categorical factual errors rather than treating every percentage move as meaningful.
6. Validate entity matching on raw answers. Common-word brands require context and disambiguation; word boundaries alone are insufficient.
7. Review sentiment labels with people, route actionable findings and compare referral traffic and conversions separately. GA4’s AI Assistant channel covers recognized referrals, excludes Google AI Overviews and AI Mode, and cannot capture mentions without clicks.

What solo brands and small teams should buy

For a narrow low-cost pilot, LLM Scout Standard is $39.99/month with ChatGPT-only coverage. Chatbeat at $99/month provides a dedicated AI workflow. Peec Starter lists $95/month for 50 prompts across three models; it does not replace social publishing or listening.

For traditional channels plus AI, budget approximately $298/month before tax for Brand24 Individual on annual billing plus its $99 AI module. For an existing SEO stack, compare the complete SE Ranking base-plus-add-on cost or Semrush’s standalone $99 toolkit against the exact engines and allowances needed. Enterprise media or social teams can request CisionOne or Lumen quotes.

Choose based on audience coverage, repeatability, raw-answer access and the work someone will do with the results. A larger prompt allowance is useful only when it measures relevant questions.

Who should delay paid LLM monitoring

Wait if customers rarely use AI assistants to research your category, a manual check already meets your needs, or nobody has time to act on the findings. Ask customers where they look for options before paying to monitor those channels.

Start with a documented pilot and review whether the evidence changes content, communications or product decisions. Expand coverage when the added information justifies its cost.

Sources

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