The 11 Best AI Visibility Tools to Track ChatGPT, Perplexity & Claude Mentions (Honest Review 2026)

Your brand appeared in 47 ChatGPT responses last month. Or maybe zero. You have no idea — and that's the problem.

Traditional analytics can measure everything that happens after someone clicks your link. Google Search Console tracks keyword rankings. SEMrush monitors SERP positions. But when ChatGPT answers a question about your category and recommends three competitors without mentioning you? That conversation is invisible to every standard tool in your stack.

This is the AI share of voice problem. AI search engines now handle billions of queries daily — ChatGPT processes 2.5 billion prompts per month, Perplexity handles 780 million monthly searches — and they're actively routing purchase decisions, vendor comparisons, and service recommendations. If you're not tracking how often your brand surfaces in these conversations, you're flying blind in a channel that already has more influence than most paid media.

AI visibility tools solve this by querying the major LLMs at scale, tracking whether your brand appears in responses to category-defining prompts, and measuring your share of voice over time. This guide reviews the 11 leading tools in that space — what they actually measure, where they fall short, and which stack fits which type of company.

What Is AI Share of Voice? (The Entity Anchor)

AI share of voice (AI SOV) measures how frequently your brand appears in AI-generated responses relative to competitors when users query topics in your category.

The concept borrows from traditional SOV measurement in paid and organic search, but the mechanics are different. Traditional SOV is about ranking position in a deterministic SERP. AI SOV is about entity presence in probabilistic language model outputs.

When someone asks Perplexity "what's the best project management tool for a 10-person startup?", the LLM constructs a response from its training data, retrieval augmentation, and current web sources. Whether your brand appears depends on:

• How often your brand is cited in credible web sources the model ingests
• How strongly your brand is associated with the right entity attributes in the model's parametric memory
• Whether your content passes threshold relevance for the specific prompt pattern

AI SOV is therefore a compound metric — part content authority, part off-site citation signals, part brand entity strength. Tracking it over time tells you whether your AEO and GEO investments are actually moving the needle.

Methodology: 4 Criteria We Used to Evaluate Each Tool

We evaluated each tool across four dimensions:

1. Coverage — Which LLMs does the tool query? ChatGPT, Claude, Perplexity, Gemini, Copilot, and Google AI Overviews each have different retrieval architectures. Tools that only query one model give you a partial picture.

2. Accuracy — Does the tool run real queries against the actual models, or estimate visibility from proxy signals like backlink profiles and structured data presence? Real queries produce real data. Proxy-based approaches are faster but less precise.

3. Reporting Depth — Can you track SOV over time? Get prompt-level breakdowns? Attribute visibility changes to specific content or citation shifts? Or do you get a single snapshot score?

4. Price/Value — What does the tool cost at meaningful query volume, and does the output justify the spend for your use case?

Quick Comparison Table

The 11 Tools: Honest Reviews

1. Peec.ai

Best for: Startups and SMBs wanting clean AI SOV tracking across the three major consumer LLMs without an enterprise price tag.

Peec.ai was purpose-built for AI visibility monitoring rather than retrofitted from an existing SEO or social listening platform. That focus shows in the product. You configure prompts that represent your category queries — "best CRM for real estate agents," "what email marketing platform does [your segment] use" — and Peec runs those queries at regular intervals across ChatGPT, Perplexity, and Gemini, returning brand mention frequency by model, query, and time period.

The SOV dashboard is the most intuitive we tested for small teams. You can see at a glance whether your brand appears in 15% of relevant ChatGPT queries or 2%, and track that number weekly. The prompt library lets you build out your category coverage without manually writing every variation.

The primary limitation is depth: Peec doesn't include Claude or Copilot in its standard query set, and the reporting stops at brand frequency. You don't get sentiment analysis on how the model characterizes your brand when it mentions you, or citation-level data showing which sources drove the mention. For brands at the stage where frequency measurement is the goal — "are we appearing at all?" — that's enough. For more advanced SOV analysis, you'll need to supplement.

Coverage: ChatGPT, Perplexity, Gemini | Real queries | ~$99/mo entry

2. Otterly.ai

Best for: Agencies and in-house teams running AEO programs who need multi-brand competitive tracking.

Otterly positions itself as the competitive intelligence tool for AI search. Where Peec focuses on your own brand's SOV, Otterly is built for tracking your position relative to the competitive set — which is typically the more valuable analysis.

The core feature is a share-of-voice breakdown across your defined competitor list for a given prompt category. If you're a fintech startup in the SMB lending space, you configure your top 10 category queries, define your 6 competitors, and Otterly runs regular LLM queries to build a competitive SOV map. You can see not just whether you appear, but whether you appear more or less frequently than Competitor A or B, and how that ratio shifts over time.

Otterly's competitive benchmarking is the strongest in this category at its price point. The prompt management workflow is also well-designed for agencies managing multiple clients — you can segment prompt sets by client without bleed between reporting views.

The downside: Otterly's reporting cadence defaults to weekly aggregates, which can make it hard to correlate specific content launches or link-building campaigns with visibility changes. The API access that would let you run custom date range queries is locked behind higher tiers.

Coverage: ChatGPT, Gemini, Perplexity | Real queries | ~$79/mo entry

3. Semrush AI Visibility

Best for: Teams already in the Semrush ecosystem who want AI Overview tracking integrated into their existing SEO workflow.

Semrush's AI Visibility feature isn't a standalone AI SOV tool — it's a module inside the Semrush Position Tracking workflow that extends keyword tracking to include Google AI Overviews. For the specific use case of Google AI Overview monitoring, it's the most robust option available.

You add your tracked keywords in Semrush as usual, enable AI Overview monitoring, and Semrush begins tracking whether your domain appears in the AI Overview box for each keyword, what percentage of queries trigger an AI Overview, and whether your brand is cited as a source. The data ties directly into your existing keyword ranking history, so you can see the relationship between your organic rank and AI Overview appearance.

The core limitation is narrow coverage: this tool only monitors Google AI Overviews. It doesn't query ChatGPT, Perplexity, Claude, or Gemini. For brands primarily concerned with Google search behavior — particularly those in categories where AI Overviews regularly appear — this integration is excellent. For brands trying to understand their LLM presence broadly, you'll need additional tools.

Pricing is an add-on to your existing Semrush subscription, which makes sense for teams already paying for the platform. If you're not a Semrush customer, this alone doesn't justify the Semrush subscription cost.

Coverage: Google AI Overviews only | SERP scraping | Add-on to existing Semrush subscription

4. Brand24 AI Mentions

Best for: Brands with active social listening programs who want to add LLM monitoring to an existing Brand24 account.

Brand24 has been a social and web listening tool for years. Its AI Mentions feature extends that listening to include ChatGPT, Perplexity, and Gemini — capturing when these models mention your brand in response to user queries, when they cite your content as a source, and when they describe your brand in specific terms.

What Brand24 does well is integration with the broader brand monitoring picture. You're getting LLM mentions alongside your social media mentions, news coverage, and forum discussions in a single dashboard. For communications teams and PR professionals who already live in Brand24, adding AI mention monitoring without switching tools is genuinely valuable.

The AI monitoring component is functional but less specialized than purpose-built AI SOV tools. The query methodology — Brand24 runs a set of category prompts rather than letting you fully customize the prompt library — limits how precisely you can measure visibility for niche or highly specific use cases. The sentiment classification for AI mentions is also weaker than the social mention sentiment analysis Brand24 is known for.

For brands with complex AI SOV measurement needs, Brand24's AI layer is supplementary. For brands that want a single tool covering social + LLM mentions and aren't doing deep AEO analysis, it works well.

Coverage: ChatGPT, Perplexity, Gemini | Real queries | ~$119/mo entry

5. Mention.com (AI Layer)

Best for: Content teams that already use Mention for media monitoring and want basic LLM mention visibility added.

Mention's AI layer follows a similar logic to Brand24's: take an existing media monitoring platform and extend it to include AI model mentions. The execution is more limited than Brand24's equivalent feature.

Mention tracks your brand's appearance in ChatGPT and Gemini responses, surfaces the context in which your brand was mentioned, and provides a basic trend line. The integration with Mention's broader keyword and media monitoring is clean.

The gaps are significant for serious AI SOV use cases. Perplexity is not currently included in Mention's AI monitoring scope. Claude and Copilot monitoring are absent. The prompt customization is minimal — you're essentially tracking reactive mentions when users happen to ask about your brand, rather than proactively measuring your SOV across category-defining prompts. The time-series reporting is limited to 30-day rolling windows in standard plans.

If you're a Mention customer and want to answer "does ChatGPT mention us sometimes?" the AI layer answers that. If you want to understand your AI share of voice relative to competitors across a defined prompt universe, this isn't the right tool.

Coverage: ChatGPT, Gemini | Real queries | ~$99/mo entry

6. Authoritas

Best for: Enterprise SEO teams that want LLM brand ranking analysis integrated with technical SEO auditing.

Authoritas occupies a distinct position in this category: it's built for enterprise SEO programs where AI visibility is one component of a larger search intelligence workflow. The LLM brand ranking tracker lets you define your category queries, run them against ChatGPT and Perplexity, and see not just whether you appear but where in the response your brand is cited — first, second, third mention — and with what frequency across query variations.

The position-within-response tracking is useful because LLMs don't return blue links with rank positions. When Perplexity recommends four vendors in response to a comparison query, the order and prominence of each mention affects click behavior. Authoritas surfaces this positional data in a way that most tools don't.

The integration with Authoritas's broader SEO platform means you can correlate LLM visibility shifts with technical SEO events, content publication dates, and link acquisition — which is the kind of analysis that lets you actually attribute changes in AI SOV to specific actions. That's valuable for teams trying to prove the ROI of AEO investments.

The pricing reflects the enterprise positioning. At ~$199/mo for meaningful query volume, Authoritas is 2–3x the cost of entry-level tools. For companies with dedicated SEO teams and AEO programs that need attribution analysis, that's defensible. For startups running basic monitoring, it's more than needed.

Coverage: ChatGPT, Perplexity | Real queries | ~$199/mo entry

7. SE Ranking AI Overviews Tracker

Best for: SEO teams that use SE Ranking as their primary rank tracking platform and want AI Overview data alongside conventional rankings.

SE Ranking's AI Overviews tracker is the Semrush AI Visibility equivalent for teams in the SE Ranking ecosystem. You track your keyword set, enable AI Overview monitoring, and SE Ranking adds a layer showing which keywords trigger AI Overviews, whether your domain appears as a source, and how that changes over time.

The implementation is technically sound. SE Ranking's SERP tracking infrastructure is well-built, and extending it to AI Overviews produces reliable data. The reporting integrates cleanly with SE Ranking's rank tracking dashboards, which means teams already familiar with the platform don't have a learning curve.

The same core limitation applies: this is a Google AI Overviews tool, not a broad LLM monitoring solution. ChatGPT, Perplexity, Claude, and Gemini web search are not covered. For brands specifically concerned with Google search behavior in AI Overview-heavy categories (health, finance, software), the coverage fits. For brands that need multi-LLM visibility, it doesn't.

SE Ranking is notably more affordable than Semrush, making this option more accessible for small teams on the SE Ranking platform.

Coverage: Google AI Overviews only | SERP scraping | Add-on to SE Ranking subscription

8. GrowthBar AEO Features

Best for: Content marketers who want AEO optimization recommendations alongside basic ChatGPT visibility tracking.

GrowthBar is primarily an AI content writing and SEO tool, and its AEO features reflect that origin: the strongest part of the product is helping you write content that's structured to be cited by AI models, not tracking whether existing content is being cited.

The visibility tracking component runs your brand name and category queries through ChatGPT and returns frequency and context data. It's functional but limited in scope — one LLM, no competitive SOV, limited time-series depth. The real value of GrowthBar's AEO feature set is the content optimization recommendations: schema suggestions, question-and-answer structuring, entity relationship guidance, all tied to a content brief generation workflow.

For teams whose primary need is AEO content strategy rather than AI visibility measurement, GrowthBar's integrated workflow — brief → content → AEO optimization → basic visibility check — is genuinely useful. For teams that want to measure AI SOV seriously, GrowthBar's monitoring depth isn't sufficient as a primary tool.

Coverage: ChatGPT | Real queries | ~$79/mo entry

9. SearchAtlas LLM Visibility

Best for: Mid-market brands that want comprehensive multi-LLM tracking with citation-level attribution in a single platform.

SearchAtlas's LLM Visibility module is one of the more technically complete options in this space for the mid-market segment. It queries ChatGPT, Perplexity, and Gemini, tracks brand mention frequency by model and prompt category, and — more usefully — attempts to attribute mentions back to the specific content pages or external citations driving them.

The citation attribution feature is the differentiator. When ChatGPT mentions your brand, SearchAtlas attempts to identify which sources in the model's retrieval chain are connected to the mention — surfacing whether the citation came from your own domain, a third-party review site, a news article, or a Wikipedia-style reference. That attribution lets you make content and link-building decisions with more precision than frequency data alone provides.

The platform also includes AEO recommendations that surface alongside the visibility data, which creates a tighter feedback loop between measurement and action.

The main criticism: the citation attribution is probabilistic rather than deterministic. SearchAtlas can surface likely source candidates for LLM mentions, but LLM retrieval is opaque enough that certainty isn't possible. The attribution is a useful signal, not a reliable fact. Teams that need to understand this limitation before building strategy around the data should build that caveat into how they use the tool.

Coverage: ChatGPT, Perplexity, Gemini | Real queries | ~$149/mo entry

10. Profound

Best for: Enterprise brands and agencies that need the most comprehensive AI visibility monitoring available across all major LLMs.

Profound is the premium option in this category, and the product justifies the price point for organizations that need it. The platform queries ChatGPT, Claude, Perplexity, and Gemini — the only tool in this review with Claude included as standard — and tracks brand visibility across all four at meaningful query volume.

The reporting depth is the strongest we evaluated. Profound gives you SOV by model, SOV by prompt category, positional data (where in the response your brand appears), sentiment and characterization analysis (not just that you're mentioned, but how you're described), and time-series tracking at daily granularity. For brands running serious AEO programs that need to attribute visibility changes to specific content launches, PR placements, or link-building campaigns, the data resolution is necessary.

The competitive intelligence layer is also more sophisticated than competitors at lower price points. You can build comparative SOV views across your full competitive set, track how competitor mentions are characterized versus your own, and identify prompt categories where you're underrepresented relative to competitors with similar authority profiles.

At ~$499/mo, Profound is priced for enterprise marketing teams and agencies with multiple clients to amortize the cost against. Startups and small teams won't be able to justify the investment for monitoring alone. But for companies treating AI search visibility as a strategic priority with budget to match, Profound is the most complete tool in the market as of mid-2026.

Coverage: ChatGPT, Claude, Perplexity, Gemini | Real queries | ~$499/mo entry

11. Rankability

Best for: SEO teams that want AEO audit and content optimization recommendations with a basic LLM visibility component.

Rankability occupies similar positioning to GrowthBar in this review — primarily an SEO and content optimization tool that has added LLM visibility features rather than a dedicated AI monitoring platform.

The AEO audit layer is the product's strongest feature: Rankability can analyze your existing content against AI-readiness criteria (structure, entity coverage, question-answer pattern density, schema implementation), score it against competitive pages, and surface specific optimization recommendations. That audit capability is useful for teams implementing an AEO program and trying to prioritize which content to optimize first.

The LLM monitoring runs across ChatGPT and Perplexity, with basic frequency and context data. Time-series tracking is available but limited to monthly snapshots in standard plans. There's no competitive SOV view included.

The right use of Rankability in an AI visibility stack is as the optimization layer — the tool that helps you determine what to change about your content to improve AI citation rates — rather than as the measurement layer tracking whether those changes worked. Pair it with a more robust tracking tool if measurement is your primary need.

Coverage: ChatGPT, Perplexity | Real queries | ~$99/mo entry

Stack Recommendation by Use Case

Startup (1–50 employees, limited SEO budget)

Primary: Otterly.ai or Peec.ai Why: Purpose-built, affordable, gets you the core SOV tracking you need without enterprise overhead. Otterly if competitive benchmarking is a priority; Peec if your primary need is tracking your own brand's frequency across the three main LLMs. Supplement: GrowthBar or Rankability for AEO content optimization guidance.

Agency (managing 5–20 client accounts)

Primary: Otterly.ai (competitive SOV) + SearchAtlas (citation attribution) Why: Otterly's multi-brand management workflow is built for agency use. SearchAtlas adds the citation attribution layer that lets you show clients which specific content or placements are driving their AI visibility. Supplement: Semrush or SE Ranking AI Overview tracking if clients are in Google-heavy categories.

Enterprise (50+ employees, dedicated SEO/content team)

Primary: Profound Why: The only tool that gives you all four major LLMs, daily time-series tracking, sentiment analysis, and competitive intelligence in a single platform. For companies treating AI visibility as a strategic metric, the data quality justifies the cost. Supplement: Authoritas for technical AEO attribution analysis; Brand24 if PR and communications teams need LLM mention data alongside social monitoring.

How to Improve Your AI Visibility Score: 5 Tactics

Tracking your AI SOV is step one. Actually improving it requires understanding why LLMs cite certain sources over others — and systematically addressing the gaps. Here are the five highest-leverage tactics based on what we've seen move the needle for clients.

1. Build the Off-Site Citation Footprint That LLMs Retrieve From

LLMs don't rely exclusively on their training data — most production deployments use retrieval augmentation, surfacing content from live web sources. The sources they retrieve from correlate strongly with the sources that have accumulated trust signals over time: authoritative industry directories, third-party review platforms, analyst coverage, and editorial coverage from established publications.

The practical implication: if you want ChatGPT to mention you when someone asks about your category, you need to appear in the sources ChatGPT trusts. For most B2B categories, that means coverage on G2, Capterra, TrustRadius, or equivalent review platforms; mentions in relevant industry round-ups from established publications; and citations in research pieces that LLMs treat as authoritative references.

Map your current citation footprint against the sources your competitors appear in. The gaps tell you where to invest in earned coverage.

2. Publish Definitive Category Content with Dense Entity Coverage

LLMs develop entity associations through repeated co-occurrence patterns in training data. If your brand name consistently co-occurs with the right category terms, product descriptors, and use cases in credible content across the web, the model builds a stronger association between your brand and those entities.

The highest-leverage content type for this purpose is the definitive category guide: comprehensive, well-structured, heavily cited content that covers a topic more completely than competitors. Not for direct traffic — for the downstream effect of getting other sites to cite your piece as a reference, which multiplies entity co-occurrence signals across the citation graph.

One post that earns 40 editorial citations is worth more for AI entity association than 40 posts that earn zero citations each.

3. Structure Content for Direct Extraction

LLMs default to extractable content when constructing responses. Content with clear question-and-answer structures, explicit definition blocks, comparison tables, and numbered lists is systematically easier for models to extract a quote or fact from than undifferentiated paragraphs.

Audit your highest-value category pages against this criterion. If a page covers 12 considerations for evaluating vendors in your category, are those 12 considerations presented as a scannable numbered list with clear headers, or buried in five paragraphs of prose? The same information, restructured for extraction, will appear in AI responses more frequently.

FAQPage schema, HowTo schema, and Speakable markup all reinforce this signal for Google AI Overviews specifically.

4. Increase Branded Mention Velocity in Credible Contexts

The rate at which your brand name appears in credible web content is a leading indicator of future AI visibility. Guest posts on relevant industry publications, podcast appearances that get transcribed and published, conference presentations that result in coverage, analyst briefings that get published as reports — each of these creates new instances of your brand appearing in contexts LLMs treat as credible.

PR and content marketing, viewed through the lens of AI entity training, is less about direct audience reach and more about the quality and credibility of the contexts in which your brand name appears. A single mention in a widely-cited industry report is worth more than 50 mentions in low-authority content.

5. Monitor Response Quality, Not Just Mention Frequency

Getting mentioned is a baseline. Getting accurately and favorably characterized is the actual goal. Tools like Profound surface not just whether you're mentioned but what the model says about you — and there are cases where a brand appears frequently but is characterized incorrectly (outdated product descriptions, wrong pricing tier, miscategorized use case) in ways that undermine rather than reinforce purchase consideration.

Run regular brand characterization checks: query your LLM tracking tools for prompts that specifically ask about your brand's capabilities, positioning, and target customers. If the model's characterization is misaligned with your actual positioning, trace it back to the source content it's drawing from and update accordingly.

The AEO Visibility Stack Framework

Synthesizing the tools and tactics above, the most effective AI visibility programs operate across three functional layers:

Layer 1 — Monitoring: Real-time and scheduled query-based tracking of brand appearance across LLMs. Tools: Profound (enterprise), Otterly/Peec (SMB), Brand24 (PR-integrated).

Layer 2 — Measurement: Attribution analysis connecting visibility changes to specific content, citation, or technical actions. Tools: Authoritas (SEO attribution), SearchAtlas (citation attribution), Semrush/SE Ranking (Google AI Overviews attribution).

Layer 3 — Optimization: Content and entity strategy adjustments informed by monitoring and measurement data. Tools: Rankability or GrowthBar (content AEO audit), in-house content and link-building programs informed by the above.

Most companies enter at Layer 1 — they want to know whether they exist in AI search at all. The compounding value comes from Layer 2 and 3: understanding why your visibility is what it is, and systematically improving it over time.

Original Resource: AI Visibility Setup Guide

We built a step-by-step AI Visibility Setup Guide for teams implementing their first LLM monitoring program — covering tool configuration, prompt library setup, competitive benchmark definition, and the first-30-days measurement workflow.

Download the AI Visibility Setup Guide (PDF) — available to Aurelius newsletter subscribers.

Bottom Line

The tool category is maturing fast. Six months ago, most of these platforms were early-access or in beta. By end of 2026, AI SOV will likely be a standard tab in every major SEO platform — the same way mobile rank tracking went from a specialty feature to table stakes between 2014 and 2017.

The brands that build measurement infrastructure now will have 12–18 months of historical data when that standardization happens — and the strategic advantage that comes from understanding a channel before your competitors start paying attention.

If you're starting today: Otterly or Peec for SOV tracking, Rankability for content audit, and a commitment to the off-site citation work that actually moves the metric.

If you want help building an AI visibility strategy — measurement infrastructure, entity content, and the off-site citation program that powers long-term LLM share of voice — we work with 20–30 brands at a time.

Related reading:

• Answer Engine Optimization (AEO): How to Become the Source AI Cites in 2026
• AI SEO Strategy: How to Rank in ChatGPT, Perplexity & AI Search
• AI Marketing Automation Services