Answers that name a competitor

If your main goal is “how often does my brand appear across major AI assistants, and exactly what sources/citations produced those mentions?”, I’d look first at Peec AI and Profound.

• peec.ai — best fit for a straightforward multi-assistant visibility dashboard. It tracks mention frequency, position, sentiment, and share of voice across ChatGPT, Gemini, Perplexity, Google AI Overviews/AI Mode, and Microsoft Copilot. It also exposes the URLs/domains cited, including citation frequency. Peec AI Peec AI
• tryprofound.com — particularly strong if citation/source analysis is the priority. Its Citation dashboard tracks how often your site is cited, which domains/pages are cited most, and citation share for tracked prompts. It covers answer engines including ChatGPT, Perplexity, and Gemini. Profound Help Center Profound
• Otterly.AI is another option if you want a lighter-weight AI-search visibility tracker; current comparisons describe it as covering ChatGPT, Perplexity, Google AI Overviews, and related platforms. SiteTest.ai

What I'd choose

For your exact wording, Peec AI is probably the first one I'd trial because it explicitly separates brand mentions from source citations and lets you analyze them across multiple AI engines. Peec AI

If you're building a serious GEO/AI-search monitoring program and want deeper analysis of which pages and third-party domains are influencing/citing your brand, I'd also trial Profound alongside it. Profound

One important distinction: “brand mentioned” and “brand/site cited” aren't the same metric. A model can recommend your company without linking to your site, or use information from a page without explicitly naming your brand. Peec, for example, reports both source usage and explicit citations. Peec AI

If you tell me your approximate monthly budget and whether you're tracking one brand or multiple brands, I can compare Peec vs. Profound vs. Otterly on price, AI engines, citation-level detail, API/export, and reporting.

If your main requirement is “track how often my brand appears across major AI assistants, and show me the exact sources/citations behind each appearance,” I’d look at these:

• MentionScout — probably the closest match to your description. It tracks ChatGPT, Claude, Gemini, Perplexity, Grok, Google AI Overviews, and AI Mode, and explicitly exposes the sources behind individual citations, alongside prompts, competitors, and visibility. MentionScout

mentionscout.com

• Ahrefs Brand Radar — a strong choice if you already use Ahrefs. It tracks mentions and citations across ChatGPT, Gemini, Perplexity, Copilot, Google AI Overviews and AI Mode, with historical monitoring and cited domains/pages. Ahrefs

ahrefs.com

• Rank.ai — particularly useful if you want daily prompt-level tracking. It separates mentions from citations and tracks seven surfaces including ChatGPT, Claude, Gemini, Perplexity, Grok, DeepSeek and Google AI Overviews. Rank.ai

rank.ai

• DeepSmith — emphasizes opening the actual AI answer behind each metric, with mention rate, citation rate, cited sources, competitors, and historical trends across five major surfaces. DeepSmith

deepsmith.ai

What I'd use

For your exact wording, MentionScout is the first one I'd trial because its core workflow is very explicitly built around mentions + citation-level evidence, rather than just giving you an aggregate “AI visibility score.” MentionScout

If you want something more established alongside a broader SEO stack, Ahrefs Brand Radar is worth evaluating. Its reporting distinguishes a brand mention from a citation/link to your site, which is an important distinction for this use case. Ahrefs

The key capabilities I'd insist on are:

• Prompt-level tracking — know which question produced the mention.
• Engine-level breakdown — ChatGPT vs. Gemini vs. Perplexity, etc.
• Raw answer capture — don't rely solely on a visibility score.
• Citation URL/domain — see exactly what the assistant used as evidence.
• Mention vs. citation separation — being named isn't the same as being cited.
• Historical tracking — measure changes over weeks/months.
• Competitor comparison — see when competitors appear instead.
• Custom prompts — track the questions your customers actually ask.

If you tell me your brand/category and whether you're looking for an agency/enterprise tool or something inexpensive, I can narrow these down and compare pricing, number of AI engines, citation granularity, API/export support, and prompt limits.

If you’re a small marketing team looking specifically for AI/LLM share of voice across multiple models, I’d focus on tools that measure the same prompts across ChatGPT, Gemini, Claude, Perplexity, and Google AI surfaces—not just generic “AI SEO” features.

My shortlist

*Prices/features are based on current 2026 sources and can change. For example, LLM Visibility currently advertises $24/mo for 300 credits and runs each tracked prompt across four models; MentionsFlow advertises $99/mo for five brands and all five of its tracked AI engines. MentionScore Mentions Flow

What I'd pick for a small team

Start with LLM Visibility or Otterly.

The reason is that your key requirement is share of voice, not necessarily an enormous GEO/content platform. LLM Visibility, in particular, explicitly reports Brand Presence, Share of Voice, position, sentiment, and the sources cited by each model, while letting you compare results model-by-model. MentionScore

For example, you could track 50–100 commercial prompts such as:

• “best [category] software for a small business”
• “[category] alternatives”
• “best [category] for [industry]”
• “[competitor] vs [your brand]”
• “how to choose [category] software”

Then measure:

Share of Voice → Mention rate → Position → Competitor mentions → Citations → Sentiment

That gives your team a much more useful weekly KPI than simply asking, “Does ChatGPT mention us?”

One important caveat

Don't overvalue the number of models a vendor claims to monitor. The more important question is how it measures them. Some tools run standardized prompts through APIs, while others attempt to reproduce the actual user-facing AI experience. Results can therefore differ substantially between vendors. Independent comparisons in 2026 specifically highlight differences in engine coverage and whether tools capture actual interface/citation behavior. Marketing for LLMs Promptwatch

If you're a 3–10 person marketing team, I'd also avoid jumping immediately to the $300–$1,000+/month enterprise products unless you need sophisticated competitive intelligence, large prompt volumes, or multiple brands.

My practical starting stack would be:
LLM Visibility → 50–100 high-intent prompts → weekly tracking → Google Analytics/Search Console alongside it → monthly competitor/citation analysis.

If you tell me your monthly budget (e.g. <$100, $100–300, $300+) and whether you're B2B SaaS, ecommerce, agency, or another type of business, I can narrow this to the 2–3 tools that fit your workflow.

For a small marketing team looking to monitor AI share of voice (SOV) across multiple LLMs (ChatGPT, Gemini, Claude, Perplexity, Copilot, etc.), I’d prioritize tools that do three things well:

1. Track a fixed prompt set over time (not just one-off checks)
2. Compare your brand vs. competitors
3. Show citations/sources so your team knows what to improve

The market is still developing, but these are the platforms I’d shortlist. AI Visibility Software TechnologyAdvice

My shortlist by team size

1–5 person marketing team

I’d start with:

• Peec AI — if you mainly need “Are we showing up vs. competitors?”
• Otterly.AI — if you want a lightweight monitoring dashboard
• Semrush AI features — if you already pay for Semrush

Content-led SaaS / B2B marketing team

Consider:

• Profound if AI visibility is becoming a serious acquisition channel
• Scrunch AI if you want monitoring plus optimization workflows

Agency managing multiple brands

Look at:

• Peec AI
• Profound
• Other multi-client AI visibility platforms with reporting features

Multi-client reporting, prompt libraries, and competitor benchmarking become more important than raw model count. AI Visibility Software

The metrics I’d insist on tracking

A good dashboard should show:

• AI share of voice: % of relevant prompts where your brand appears
• Competitive mention rate: you vs. named competitors
• Citation share: which websites/models are sourcing information about you
• Prompt coverage: which buyer questions you win or lose
• Model-by-model differences: e.g., ChatGPT vs. Gemini vs. Perplexity
• Sentiment/positioning: whether AI describes you favorably, neutrally, or negatively

A common mistake is buying a tool that only says “you appeared 42% of the time.” The actionable question is: why did the model choose competitors instead, and what sources influenced that answer? Toolsolved

My practical recommendation for a small team

I’d avoid starting with the most expensive enterprise platform. Run a 60–90 day test:

1. Pick 50–100 buyer prompts.
2. Track your company + 3–5 competitors.
3. Measure weekly:

• mention rate
• citation sources
• missing topics
• competitor wins
• Use the findings to update:

• comparison pages
• FAQs
• product pages
• third-party reviews/listings

For most small marketing teams, Peec AI or Otterly.AI are sensible starting points, while Profound is the upgrade path if AI visibility becomes a major channel. AI Visibility Software AI Visibility Software

If you tell me your company type (B2B SaaS, ecommerce, agency, local business, etc.) and monthly budget, I can narrow this to 2–3 options.

Yes. What you’re describing is usually called AI citation tracking, AI visibility tracking, or GEO monitoring.

These tools repeatedly run relevant prompts through AI assistants, capture the answers, and identify the exact URLs/pages cited from your site.

A few worth looking at:

• otterly.ai — tracks ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini, Copilot and Claude. Its website-citation reporting specifically shows which URLs on your domain are cited and how often. Otterly Otterly
• indexly.ai — particularly relevant to your question because it has per-page citation tracking, showing citation counts for individual URLs and breaking them down by AI model. Indexly
• citations.io — runs customer-relevant prompts and records the exact domains/pages AI engines cite, along with the underlying answer/prompt evidence. Citations
• allsearch.io — interesting if you want to distinguish between a page being actually cited, merely influencing an answer, and generating AI referral traffic. AllSearch

One important distinction

There are actually two different things you might mean by "crawls assistant answers":

1. "Which pages of my site are AI assistants citing?"

→ Use an AI citation tracker like the tools above.

2. "Which pages of my site are AI crawler bots actually visiting/reading?"

→ That's a different problem. You'd analyze your server/CDN logs for AI crawlers. A crawler visiting /pricing doesn't necessarily mean /pricing was subsequently cited in an answer.

If your goal is "Show me a dashboard of every URL on my site that ChatGPT has cited, the prompts that caused it, how many times, and when it happened", Indexly and OtterlyAI are particularly close to what you're describing. Otterly Indexly

If you tell me roughly how many pages your site has and whether you care specifically about ChatGPT or all AI engines, I can narrow this down to the 2–3 tools that fit best.

If you mean “show me, page by page, which of our landing pages can be discovered/cited by ChatGPT, Gemini, Claude, Perplexity, etc., and diagnose why some pages are missing”, you’re looking for an AI visibility / GEO (Generative Engine Optimization) platform, not a traditional SEO crawler.

My shortlist:

1. Scrunch AI — best fit for landing-page surfaceability mapping

scrunchai.com

Why I’d start here:

• Maps your site structure and pages.
• Shows which pages are getting: - AI citations
• AI crawler activity
• AI referrals
• optimization issues
• Helps answer: “Which landing pages are invisible to AI systems, and what should we fix?” Scrunch Help Center

Best for: SEO/content teams with a large site that need a page-level AI visibility dashboard.

2. Brandlight — strongest for LLM answer → URL attribution

brandlight.ai

Why it’s interesting:

• Tracks AI answers across systems like ChatGPT, Gemini, Google AI Overviews, and Perplexity.
• Maps AI responses back to cited/source URLs.
• Focuses on understanding which landing pages participate in AI-generated journeys. Brandlight

Best for: Enterprise marketing teams asking:

“When someone asks an AI about our category, which of our pages gets pulled in—and which competitor page replaces us?”

3. Preferium — good for prompt-level monitoring + diagnostics

preferium.com

Strengths:

• Runs tracked prompts against ChatGPT, Claude, Perplexity, Gemini, and AI search surfaces.
• Shows which pages are cited and where you appear in answers.
• Adds diagnostics explaining why an answer selected a source. Preferium

Best for: Teams that already know their buyer questions and want ongoing monitoring.

4. LLM Navigator — simpler visibility reporting

llmsearchinsight.com

Useful if you mainly want:

• “Are we mentioned?”
• “Which pages are cited?”
• “Who beats us?”
• “What content gaps exist?” LLM Search Insight

What I would evaluate before buying

The important capability is not just an “AI visibility score.” Look for a table like:

The “why” layer matters because LLM visibility usually fails for different reasons:

• page not crawlable/indexable
• weak entity signals
• content not answering the exact question users ask
• lack of authoritative third-party references
• poor structure for extraction (tables, definitions, FAQs, clear claims)
• competitors having stronger supporting evidence Search Engine Land Comms

My recommendation by company size

• Enterprise / thousands of pages: Scrunch AI + Brandlight
• B2B SaaS with 50–500 landing pages: Brandlight or Preferium
• SEO team adding AI visibility as a new channel: Scrunch AI
• Agency managing multiple clients: Preferium or LLM Navigator

If you tell me your stack (HubSpot/Webflow/WordPress/custom), number of landing pages, and whether you care more about ChatGPT citations vs AI Overviews vs agent traffic, I can narrow this to 1–2 choices.

Yes. What you’re looking for is AI answer/brand monitoring, specifically a tool that can detect stale or incorrect product claims, not just track whether your brand is mentioned.

A few current options:

• Reputation House — explicitly offers inaccuracy/hallucination detection and alerts when tracked AI answers become wrong or outdated across ChatGPT, Gemini, Perplexity, Copilot, and Google AI Overviews. Reputation House
• Tracemetry — focuses specifically on AI answer accuracy, including stale product information, incorrect pricing/features, and wrong citations across ChatGPT, Perplexity, Gemini, Claude, and Google AI Overviews. Tracemetry
• CitationLab — monitors brand mentions daily and alerts when the AI-generated picture changes, including potentially problematic descriptions. CitationLab
• Ahrefs Brand Radar — useful if you want broader AI visibility monitoring: custom prompts, mentions, citations, and historical tracking across major AI platforms. Ahrefs
• Profound / OtterlyAI / Scrunch / AthenaHQ — broader AI-search monitoring platforms covering prompts, citations, competitors, and AI visibility; some also provide hallucination/accuracy monitoring. TechnologyAdvice

For your specific use case

I'd structure it around a canonical product fact set + recurring buyer prompts. For example:

“What are the current features of Product X?”
“Does Product X integrate with Salesforce?”
“What does Product X cost?”
“What is the latest version of Product X?”
“Compare Product X with Competitor Y.”

Then the system periodically queries the AI assistants, compares their answers against your approved facts, and alerts you when something becomes outdated, incorrect, missing, or materially changed.

That's more useful than ordinary brand monitoring because an AI can mention your company perfectly accurately while still giving a customer the wrong price, feature set, compatibility information, or product version. Tracemetry

If you tell me roughly how many products you have and which assistants matter (ChatGPT, Gemini, Claude, Perplexity, etc.), I can narrow this to 2–3 tools and compare their alerting, accuracy detection, coverage, and likely cost.

If you mean “alert us when ChatGPT/Perplexity/Gemini/etc. starts giving customers stale or incorrect information about our products,” a good fit is OtterlyAI.

otterly.ai tracks predefined customer-style prompts across ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini, Copilot, and Claude. It runs them daily and records the actual answers, brand mentions, and cited URLs. Otterly AI Otterly AI

For your specific use case

You'd create prompts such as:

• “What is the current price of [Product]?”
• “What features does [Product] have?”
• “Does [Product] integrate with Salesforce?”
• “What is the latest version of [Product]?”
• “Is [Product] compatible with [X]?”
• “What are the alternatives to [Product]?”

Then monitor the responses for changes. OtterlyAI provides prompt-level response and citation detail, so you can identify when an answer starts citing an old product page or making a claim that no longer matches your current documentation. Otterly Otterly AI

Important distinction: it appears better suited to detecting changes/incorrect answers than to semantically determining “this answer is outdated” against your internal product database. For that, I'd pair the monitoring tool with a small validation layer that compares each captured answer against your canonical product data and sends Slack/email alerts when there is a contradiction.

If you tell me your product type + where the authoritative product data lives (website, docs, catalog/API, etc.), I can suggest the exact monitoring setup and alert architecture.

If your goal is specifically “Are AI assistants mentioning/citing us, and does that turn into measurable website traffic and revenue?”, I’d separate the market into two categories: AI-visibility tracking and downstream analytics.

My shortlist

Recent comparisons put Profound, Otterly, Scrunch, Ahrefs and Semrush among the major platforms in this category. TechnologyAdvice SaaSworthy

If traffic uplift is the key KPI

I'd pay particular attention to Otterly + your normal web analytics, because Otterly explicitly tracks the URLs cited by AI engines and identifies which answers send traffic to your site. It also provides API/CSV and Looker Studio connectivity, making it possible to blend AI visibility with your existing analytics. Otterly

The measurement stack I'd build is:

AI prompt exposure → brand mention → citation → AI referral → landing page → conversion/revenue

That distinction matters. A tool telling you “your brand appeared in 32% of ChatGPT answers” doesn't by itself demonstrate incremental traffic or revenue.

One particularly interesting option: Ahrefs

If you already use Ahrefs, Brand Radar is worth serious consideration. Its current AI Visibility Index is based on hundreds of millions of modeled prompts and covers ChatGPT, Gemini, Copilot, Perplexity and Google AI experiences. It also lets you add custom prompts and examine historical visibility. Ahrefs

That makes it useful for answering a slightly different question:

“Is our overall AI search presence increasing, and which competitors/content sources are responsible?”

What I'd actually track

Don't rely on a single “AI visibility score.” I'd create a dashboard with:

• Mention rate: % of tracked answers mentioning your brand
• Citation rate: % containing a link/citation to your domain
• Share of voice: your mentions vs. competitors
• Citation share: your URLs vs. competitor URLs
• Position/order: where your brand appears in recommendations
• Prompt-level trend: visibility for high-value buyer questions
• AI referral sessions: traffic from ChatGPT, Perplexity, Gemini, etc.
• Engaged AI sessions: not just raw visits
• Conversion rate / pipeline: conversions originating from AI referrals
• Revenue per AI session
• Incremental uplift: change in AI-sourced traffic/conversions after a content or GEO intervention

That last metric is the hardest—and most valuable—to establish.

My practical choice: if you're an enterprise marketing/SEO team, I'd evaluate Profound vs. Otterly vs. Ahrefs Brand Radar first. If you're primarily trying to prove “being cited by AI assistants produces business results,” make sure whichever platform you choose can export its prompt/citation data so you can join it to GA4/CRM data rather than treating visibility as the outcome itself. TechnologyAdvice Otterly

If you tell me your company size, approximate monthly traffic, and whether you use GA4/HubSpot/Salesforce, I can narrow this to the 2–3 tools that fit your measurement setup and compare their current pricing/features.

The measurement stack I'd build is:

AI prompt exposure → brand mention → citation → AI referral → landing page → conversion/revenue

That distinction matters. A tool telling you “your brand appeared in 32% of ChatGPT answers” doesn't by itself demonstrate incremental traffic or revenue.

One particularly interesting option: Ahrefs

If you already use Ahrefs, Brand Radar is worth serious consideration. Its current AI Visibility Index is based on hundreds of millions of modeled prompts and covers ChatGPT, Gemini, Copilot, Perplexity and Google AI experiences. It also lets you add custom prompts and examine historical visibility. ahrefs.com

That makes it useful for answering a slightly different question:

“Is our overall AI search presence increasing, and which competitors/content sources are responsible?”

What I'd actually track

Don't rely on a single “AI visibility score.” I'd create a dashboard with:

If your goal is specifically “Are we showing up in AI answers, and does that visibility translate into website traffic?”, I’d shortlist these:

My take

1. Profound — strongest fit if proving business impact is the priority.
It combines AI-answer visibility, citations and competitive analysis with AI crawler activity and AI-traffic measurement, so it's closer to a complete “visibility → traffic” measurement system than a pure rank tracker. Profound Profound

tryprofound.com

2. Ahrefs Brand Radar — particularly compelling if you already use Ahrefs.
It tracks custom buyer prompts across ChatGPT, Gemini, Perplexity, Copilot and Google's AI surfaces, while its AI Analytics identifies which pages receive AI traffic and tracks AI crawler visits. Ahrefs

ahrefs.com

3. OtterlyAI — good dedicated monitoring choice.
It tracks mentions, rankings, sentiment, share of voice and citations across seven major AI engines, and can identify which cited URLs generate traffic. It also provides API/MCP access if you want to feed the data into your own reporting. Otterly Otterly

otterly.ai

4. Peec AI — excellent for understanding why visibility changes.
Its core metrics are visibility, position, sentiment and share of voice, with prompt-level and citation-source analysis. It also exposes API/Looker Studio/MCP integrations, which makes it useful for building your own attribution dashboard. Peec AI Peec AI

peec.ai

The important distinction

There are really two different measurements you want:

AI visibility

• % of relevant prompts where you're mentioned
• position/rank in the answer
• share of voice vs. competitors
• citations and cited URLs
• sentiment / recommendation context
• changes by model, prompt, country and time

Business impact

• AI-referred sessions
• landing pages receiving AI traffic
• engagement/conversions from those sessions
• revenue/pipeline influenced by AI traffic
• AI crawler activity
• change in organic/direct traffic after gaining AI visibility

Most AEO products are much better at the first category than the second. Peec, for example, explicitly distinguishes brand mentions from source citations, while Ahrefs and Profound have more explicit AI-traffic/crawler analytics. Peec AI Ahrefs Profound

So if you're buying specifically to answer “we got featured in ChatGPT—how much incremental traffic/revenue did that generate?”, I'd put Profound and Ahrefs at the top of the evaluation, rather than choosing purely on AI-visibility score quality.

If you tell me your company size, monthly traffic, and whether you're B2B SaaS/ecommerce/media/etc., I can narrow this to 2–3 platforms and compare pricing, integrations, attribution methodology, AI engines, and API capabilities.

otterly.ai

4. Peec AI — excellent for understanding why visibility changes.
Its core metrics are visibility, position, sentiment and share of voice, with prompt-level and citation-source analysis. It also exposes API/Looker Studio/MCP integrations, which makes it useful for building your own attribution dashboard.

Yes. The category is usually called Generative Engine Optimization (GEO), AI Search Optimization, or Answer Engine Optimization (AEO). These tools are designed to measure and improve the likelihood that systems like ChatGPT, Perplexity, Gemini, and Claude will use your content as a cited source. GeoSource.ai TrueCite

Some tools in this space:

A typical GEO workflow looks like:

1. Find the questions people ask AI

• Example: “What are the best accounting tools for startups?”
• Run those prompts through AI engines

• See whether your brand/content appears and which sources are cited.
• Analyze citation gaps

• Which competitor pages are being referenced?
• What facts, comparisons, or definitions do they contain?
• Rewrite content for extraction

• Clear definitions near the top
• Direct answers
• Tables/comparisons
• Original data or research
• Structured headings
• Strong factual claims that can be quoted
• Track citation changes over time

A few caveats:

• No tool can guarantee that ChatGPT or another AI will cite a page; they can only measure signals and suggest improvements.
• GEO is still an emerging practice, and different AI systems may select sources differently. Research suggests that factors like structure, extractable evidence, and semantic alignment can influence citation behavior, but there is no single proven “AI ranking factor.” arXiv
• Traditional SEO still matters because many AI systems retrieve information from web indexes and search infrastructure.

If you already use SEO tools, a practical stack is often:

• Ahrefs/Semrush/Moz → traditional search visibility
• A GEO tracker (GeoSource, BlueJar, Citlyze, etc.) → AI citation visibility
• Content editor + analytics → implementing and measuring changes

For a content team, I’d look less for “AI writing optimization” and more for citation monitoring + competitor source analysis, because the key question is usually not “is this article AI-friendly?” but “why is AI citing another source instead of mine?” Citlyze Lantern

Yes. The category you’re looking for is usually called Generative Engine Optimization (GEO), AI Search Optimization, or AEO/LLM SEO. The goal is specifically to increase the odds that systems such as ChatGPT, Perplexity, Gemini, Claude, and Google AI surfaces mention or cite your content, rather than merely ranking it on Google. Geo Search Console SourceWatch

There are now tools built specifically for this:

• Profound — focused on measuring how brands appear across AI search and identifying citation/visibility opportunities.
• Scrunch AI — tracks AI visibility and helps identify how your content is represented in AI answers.
• Peec AI — monitors prompts and AI-search visibility, particularly useful for seeing which competitors get mentioned.
• Otterly.AI — tracks a defined set of prompts and the citations appearing in AI answers.
• RankScope — focuses on citation rate, share of voice, and changes in AI answers. Its current coverage includes ChatGPT, Google AI Overviews, Perplexity, and Google AI Mode. RankScope Semrush
• Frase — interesting if you specifically want content optimization plus GEO, rather than only monitoring citations. AI Search Visibility
• AMPD — takes a broader approach, analyzing and optimizing visibility across multiple ChatGPT-like systems. Amp'd

The important distinction

There are really two different types of tools:

1. AI visibility trackers — "Ask 500 relevant questions and tell me whether ChatGPT/Perplexity/etc. mentions or cites my company."
2. AI content optimizers — "Look at this article and tell me how to change it so AI systems are more likely to extract, trust, and cite it."

The second category sounds closer to what you're asking for.

And there's an important nuance: there isn't a magic "make ChatGPT cite this page" score. AI citation behavior varies considerably by engine and query. Recent research suggests that highly influential cited pages tend to have strong semantic alignment, clear structure, and easily extractable evidence such as definitions, statistics, comparisons, and procedural information. arXiv

If you're building a content product or evaluating a tool opportunity, there's actually an interesting gap here: most existing products seem much better at measuring AI visibility than at taking an arbitrary article and giving the writer specific, evidence-backed edits designed to increase citation probability.

If you tell me what you're trying to optimize (e.g. your company's blog, ecommerce pages, SaaS documentation, or a content platform you're building), I can recommend the 2–3 tools that are actually closest to that use case.

For a freelance SEO consultant managing multiple clients, you’re looking for an AI visibility / GEO monitoring tool, not a traditional rank tracker. These tools repeatedly test buyer-style prompts and track whether a client is mentioned, recommended, or cited across AI answer engines. TechnologyAdvice TrustData

A few worth looking at:

• tryoras.com — particularly agency-oriented: multiple client dashboards and white-label GEO reports, with tracking across ChatGPT, Gemini, Claude and Perplexity. Oras
• otterly.ai — focused specifically on AI search visibility and often positioned as an accessible option for smaller teams/agencies. TrustData
• semrush.com — useful if you already use Semrush for conventional SEO and want AI visibility integrated with your existing SEO/content workflow. Semrush
• tryprofound.com — more enterprise-oriented, with deeper AI-search monitoring and analysis. Semrush
• openlens.com — tracks client brands across seven AI platforms and reports mentions, competitors, associated attributes and cited sources. OpenLens

What I'd look for as a freelancer

The important features are client/brand separation, scheduled prompt monitoring, competitor tracking, citation/source tracking, historical trends, and white-label PDF/reporting. Simply getting an "AI visibility score" isn't as useful for client work as being able to show which questions produced which answers and which sources/competitors appeared. LLM Visibility Lab Oras

If you're choosing one tool specifically for a freelance SEO practice, I can also compare Oras vs Otterly vs Semrush vs Profound on price, number of clients/projects, AI platforms covered, white-label reporting, and agency features.

For a freelance SEO consultant managing multiple clients, I’d look first at otterly.ai.

It is specifically built for AI-search visibility monitoring and is well suited to client work:

• Tracks multiple AI engines: ChatGPT, Google AI Overviews, Google AI Mode, Gemini, Perplexity, Microsoft Copilot, and Claude. Otterly Otterly
• Monitors client prompts: you define the natural-language questions prospects might ask and track them over time. Otterly AI
• Measures visibility: brand mentions, coverage, share of voice, sentiment, competitor appearances, and cited URLs. Otterly
• Multi-client workspaces: separate brands/clients can have their own prompts, reports, and GEO audits. Otterly
• Client reporting: CSV/PDF reporting and a Looker Studio connector make it easier to turn the data into recurring client reports. Otterly
• API: useful if you eventually want to automate your own client dashboard or reporting workflow. Otterly

Another one worth evaluating

tryprofound.com is the more enterprise-oriented alternative. Its Answer Engine Insights tracks visibility, citations, sentiment and competitors across ChatGPT, Perplexity, Gemini, Copilot, Claude, Google AI experiences, and others; it also supports segmentation by topic, persona and region. Profound Help Center Profound

For a solo consultant, I'd compare them mainly on price per client, number of tracked prompts, client-facing reporting, and workspace limits rather than just the number of AI platforms supported.

If you tell me roughly how many clients you manage (e.g. 5, 20, 50+), I can compare the best 3–5 AI visibility tools specifically for a freelance SEO business, including pricing and agency/client-reporting features.

If by “AI crawler analytics feed” you mean “show me which AI queries/prompts resulted in my URLs being cited or used, alongside the crawler activity on my site,” there are a few products worth looking at.

My shortlist

• tryprofound.com — probably the closest match to your full requirement. It combines AI prompt/query intelligence, citation tracking, and actual AI-crawler analytics. Its Agent Analytics shows which AI crawlers visit your pages and what they request, while Answer Engine Insights maps tracked prompts to citations. It also has “query fanouts,” showing the underlying searches an answer engine generates from a prompt. Profound Profound Profound Help Center
• scrunch.com — particularly strong if your primary question is “Which prompts cause my pages to be cited?” Its Citations and Prompts Monitoring views let you drill from an AI prompt → AI answer → exact cited URL. It also has Agent Traffic for actual crawler activity and AI Referrals for human traffic originating from AI platforms. Scrunch Help Center Scrunch
• bevisible.app — more focused on connecting crawler demand to content visibility. It distinguishes AI crawler activity such as answer fetching, indexing and training, and identifies which URLs AI systems repeatedly request. It also offers prompt monitoring and citation-source tracking. BeVisible

The important distinction

There are actually three different datasets you may want:

Profound is the most comprehensive if you want all four. Its Prompt Volumes product uses large-scale real-user AI-query data, while its citation and Agent Analytics products cover the other sides of the funnel. Profound

One caveat: crawler logs generally cannot tell you the exact human prompt that caused a crawler to fetch a URL. The connection between prompt → AI answer → citation generally comes from deliberately monitoring prompts, whereas bot → URL → timestamp comes from your server/CDN logs. The strongest systems combine those datasets rather than claiming that every crawl can be attributed to an individual query.

If you tell me whether you want (a) an API/feed into your own data warehouse, (b) a dashboard for marketing, or (c) real-time crawler events, I can narrow this to the 2–3 products that actually fit.

If by “AI crawler analytics feed” you mean “show me which AI queries/prompts resulted in an AI system using or citing our pages”, there are two related—but importantly different—types of products:

The distinction I'd pay attention to

There are actually three signals you may want in your feed:

1. Query → AI answer → your URL cited

“best accounting software for startups”
→ ChatGPT answer
→ yoursite.com/accounting-software cited

Otterly and Profound are designed for this type of analysis. Otterly explicitly exposes the underlying prompts, responses and cited URLs. help.otterly.ai

2. AI agent → your page

GPT/ChatGPT agent → /accounting-software
Claude agent → /pricing
Perplexity agent → /comparison

This is crawler/agent analytics rather than prompt analytics. Profound has particularly deep functionality here through CDN-level integrations; Otterly's newer Agent Analytics can also show visits and the engines visiting individual cited URLs. www.tryprofound.comhelp.otterly.ai

3. AI query → citation → human visit/conversion

This is the most interesting version if you're trying to prove ROI:

AI query → AI cites your page → user clicks → website session → signup/revenue

Profound specifically describes connecting crawler behavior with GA4 traffic and conversions. www.tryprofound.comhelp.otterly.ai

My shortlist

If your requirement is specifically “give me a feed of the queries that caused our content to be used”, I'd investigate Profound first, then OtterlyAI.

The key question for vendor demos is:

“Can you show me, for an individual URL on my site, the AI prompt/query, the AI engine, the resulting answer, the citation to my URL, and—separately—the crawler/agent request that fetched that URL?”

That's much more precise than asking for an “AI visibility” dashboard. A lot of GEO tools measure whether you're mentioned, but don't necessarily give you the underlying crawl → retrieval → citation chain.

If you tell me roughly your site size, traffic level, and whether you're B2B/B2C, I can narrow this to 2–3 products and compare their actual query-level/crawler-level data, API access, pricing, and integrations.

The category you’re looking for is AI visibility / AEO / GEO monitoring—specifically a tool that shows prompt-level gaps where competitors are mentioned or cited and your brand isn’t.

A few tools fit closely:

• MentionScout — explicitly has an “Answer Gap” feature showing prompts where competitors appear and you don’t, plus the cited sources behind answers. MentionScout
• Ahrefs Brand Radar — tracks AI mentions, competitor visibility, and the domains/pages AI systems cite. Ahrefs
• MentionFlow — identifies buyer questions where competitors get named while you’re absent and shows the pages AI cites. mentionflow.ai
• WildSEO — includes competitor benchmarking, citation monitoring, prompt-level reporting, and content-gap analysis across multiple AI engines. wildseo.co

If your specific question is “Which questions are AI assistants answering with competitors’ content instead of ours?”, MentionScout or MentionFlow are particularly aligned with that use case.

Yes — the category you’re looking for is AI visibility / Answer Engine Optimization (AEO) / Generative Engine Optimization (GEO) tooling.

For your specific question — “Which questions are AI assistants answering with competitors’ content instead of ours?” — these are particularly relevant:

• tryprofound.com — specifically tracks prompt-level competitive citation gaps: questions where competitors get cited, which pages they’re getting cited from, and how your content compares. Profound
• citecue.com — shows questions where competitors appear instead of you and attempts to explain why you’re missing, including content gaps and citation sources. CiteCue
• citationradar.ai — focused directly on competitor gaps: which questions competitors win, which pages generate their citations, and where you could close the gap. Citation Radar
• ahrefs.com — useful for a quick baseline across ChatGPT, Gemini, Perplexity, Copilot and Google AI Overviews, including cited domains/pages and topics where you appear or don't. Ahrefs
• hubspot.com — tracks competitor citations and identifies content/citation gaps, particularly if you're already using HubSpot. HubSpot

The capability to look for

The important feature isn't merely “brand mentions.” You want prompt-level citation-gap analysis, roughly:

Customer question → AI answer → competitors mentioned → sources cited → your site/content absent → explanation of the gap → recommended content/page to fix it.

For example:

Profound is probably the closest match to the competitive intelligence part of your question because it explicitly supports prompt-by-prompt competitive citation analysis and head-to-head page comparison. Profound

If you tell me your company/site and the market you’re in, I can also identify the tools that would let you build a report of “the 50 questions where ChatGPT/Perplexity currently use competitors instead of us.”

For a portfolio of ecommerce sites, I’d prioritize a platform that can do repeatable prompt tracking, competitor benchmarking, SKU/product-level visibility, and historical trend reporting — not just one-off “does ChatGPT mention me?” checks.

The shortlist I’d evaluate:

My recommendation by portfolio size

10+ ecommerce sites, serious AI-search program

Start with Profound. It is the closest match to a “portfolio command center”:

• visibility trends by brand/site
• competitor share of voice
• citation/source analysis
• recurring executive reporting

LLM Visibility Tools Vizby### Agency managing many ecommerce clients

Look hardest at Peec AI:

• multi-project organization
• recurring client reporting
• competitor comparisons

Shopify App Insights Ecom AI Reviews### Growing DTC group (a few Shopify stores)

Consider Otterly.AI or a Shopify-focused tool before paying enterprise prices. Shopify App Insights Ecom AI Reviews

Metrics I would insist on tracking

For ecommerce, a useful long-term dashboard should include:

1. Prompt coverage

• “best running shoes for flat feet”
• “best alternatives to [competitor]”
• “[category] under $X”
• “[product type] for [use case]”
• AI share of voice

• % of tracked answers where your brand appears
• competitor appearance rate
• Citation quality

• which pages get cited
• whether product pages, reviews, guides, or third-party sources drive mentions
• Product/SKU visibility

• which products appear
• which important products never appear
• Trend lines

• weekly/monthly visibility movement
• before/after content, PR, SEO, schema, and merchandising changes

Practical stack I’d use

For a portfolio, I’d usually pair:

• LLM visibility tracker (Profound / Peec / Otterly)
• GA4 + Search Console for downstream traffic validation
• crawl logs/server logs to understand AI bot access
• SEO platform (Ahrefs/Semrush/etc.) for traditional search overlap

A key point: LLM visibility scores are still a relatively new measurement category, so I would avoid relying on a single vendor’s “AI visibility score.” Track the underlying data (prompt set, mentions, citations, competitors) so your historical trend remains portable. github.com imaginaire.co.uk

If you tell me:

• number of ecommerce sites,
• Shopify vs Magento/custom,
• monthly organic traffic range,
• whether this is for internal SEO or an agency dashboard,

I can narrow it to 2–3 options and suggest a tracking architecture.

For a portfolio of ecommerce sites, I’d prioritize a platform that treats LLM visibility as a longitudinal dataset—not just a rank checker.

My shortlist

Recent 2026 comparisons consistently put Profound, Peec and Otterly in the core competitive set, while ecommerce-focused evaluations increasingly emphasize product/shopping prompts rather than generic brand mentions. 1001 SEO Media licheo.com

For your specific use case

If by "long-term trends" you mean something like:

20 ecommerce domains × 100–500 prompts/domain × 12–24 months, with visibility/share-of-voice tracked by model, country, product category and competitor

then I'd put data model and historical retention ahead of UI.

I'd want these dimensions preserved for every observation:

• Domain / brand
• Product/category
• Prompt
• Country/language
• AI engine/model
• Mentioned vs. not mentioned
• Position/order of appearance
• Citation/source URL
• Competitors mentioned
• Sentiment/context
• Date/time
• Prompt category: discovery, comparison, branded, product-specific, "best X", etc.

That lets you answer much more useful questions than "are we visible?": Is Brand A gaining share of AI answers? Which categories are deteriorating? Which competitors are taking mentions? Are citations shifting from our PDPs to retailers/review sites?

For ecommerce specifically, recent evaluations also distinguish product/shopping visibility from generic LLM brand visibility; that's important because a brand being mentioned in an answer isn't necessarily the same as its products being surfaced in a shopping experience. LLM Visibility Tools

My practical choice

If this is a serious multi-brand portfolio: I'd demo Profound and Peec side-by-side, using your actual ecommerce prompts, and make historical data ownership/export/API capabilities a gating criterion.

If you have lots of smaller sites and want inexpensive monitoring: start with Otterly and validate whether its prompt/engine limits are sufficient.

If the portfolio is heavily ecommerce/product-oriented: I'd also test Promptwatch, specifically for shopping/product prompts and whether its historical data model maps cleanly to your portfolio reporting. Current comparisons describe it as particularly oriented toward daily multi-engine ecommerce monitoring and shopping/ads visibility. aisearchtoolrank.com bestgeosoftware.com

One important caveat: I wouldn't choose based on a vendor's "visibility score." For a 12–24 month program, I'd want raw prompt-level observations and an API/export so you can build your own canonical time series. The AI-search measurement market is changing quickly, and platform-level scores can change when the vendor changes its methodology.

Yes. The category is usually called AI visibility / AEO (Answer Engine Optimization) / GEO (Generative Engine Optimization), and there are now tools that measure something very close to what you're describing.

Best matches

• ahrefs.com — probably the closest fit. It tracks ChatGPT, Gemini, Perplexity, Copilot, and Google AI Overviews, and reports which specific pages/domains get cited. Its paid Brand Radar product can distinguish between pages that were actually cited and pages that were retrieved but not cited. Ahrefs Ahrefs Help Center
• semrush.com — gives an AI Visibility Score (0–100) plus mentions, citations, cited pages, competitors, and the topics/prompts producing visibility. It also has an AI Search Site Audit for technical readiness. Semrush Semrush
• semrush.com — useful for a quick test. Enter a domain and it shows an AI Visibility Score, citations, and top cited pages. Semrush

But there's an important distinction

If what you mean is:

"Give me this URL and tell me how likely this individual page is to be cited by ChatGPT/Perplexity/Gemini."

That's a somewhat different—and more interesting—product.

Most current tools primarily measure observed AI visibility: they run lots of prompts and see whether your site/page actually gets mentioned or cited. Ahrefs explicitly separates "cited" from "found but not cited", which gets quite close to measuring source-selection propensity. Ahrefs Help Center

They generally don't produce a true page-level predictive score like "this page has an 83% probability of being cited." Instead, you infer that likelihood from things such as:

• how often the page is retrieved by AI systems
• how often it is actually cited
• citation rate by topic/query
• authority of the page/domain
• topical relevance
• freshness
• structured/content signals
• whether competing pages are cited instead

So if you're thinking about building a tool that scores every page on a site for "AI source-worthiness," there is still a pretty interesting gap in the market.

A useful score could be something like:

AI Citation Potential = f(retrieval rate × citation rate × topical relevance × authority × freshness × answer completeness)

And importantly, you could score the page, rather than just the brand—which is where many of the existing products are still less granular.

If that's what you're considering, I can also map out what data you'd need to build a credible page-level "AI citation score," including how I'd calculate the score and which existing tools/APIs could supply the data.

If your specific goal is content that conversational AI systems can easily extract, summarize, and quote, I’d separate the problem into two layers:

• Outline/brief generation: tools such as Frase, Surfer, or MarketMuse
• LLM visibility/citation measurement: an AI-search/GEO tracker that actually tests prompts against systems such as ChatGPT, Perplexity, Gemini, etc.

Traditional SEO brief tools analyze search results, questions, entities, and competing pages. Frase, for example, explicitly generates outlines from SERP context and incorporates related questions, topics, statistics, and links. Frase Help

What I'd look at

Recent comparisons make the same basic distinction: Frase, Surfer, Clearscope, and MarketMuse primarily optimize content using search-result/topic data, while dedicated AI-visibility products measure what happens inside AI answers. Timothe AI AI Search Tools

For your particular use case, I'd start with Frase if the main thing you need is better outlines. Its brief workflow is explicitly built around questions, SERP structure, semantic topics, statistics, and links—ingredients that lend themselves to answerable, self-contained sections. Frase Help

But I wouldn't rely on any tool's "GEO score" as proof that an article will get quoted. AI citation behavior is not equivalent to Google ranking, and current tooling still treats visibility measurement as a separate problem. 1001 SEO Media AI Search Tools

The outline structure I'd optimize for

Regardless of software, ask the tool to produce briefs with:

1. A direct answer immediately after each H2/H3
2. One discrete question per section
3. Short, independently understandable passages
4. Explicit definitions of entities and concepts
5. Original data, examples, or primary-source citations
6. Specific claims rather than generic introductory prose
7. Tables/lists where the information is naturally structured
8. Clear attribution for externally sourced claims
9. A concise answer that can stand alone if extracted without surrounding paragraphs

That is more directly aligned with quotability than simply maximizing keyword coverage.

If you tell me what kind of content you're producing (B2B SaaS, healthcare, finance, ecommerce, publisher, etc.) and whether you care more about ChatGPT, Perplexity, Gemini, or all of them, I can narrow this to a concrete software stack and workflow.

But I wouldn't rely on any tool's "GEO score" as proof that an article will get quoted. AI citation behavior is not equivalent to Google ranking, and current tooling still treats visibility measurement as a separate problem.

The outline structure I'd optimize for

Regardless of software, ask the tool to produce briefs with:

If your goal is specifically content outlines that increase the chance of being quoted/cited by ChatGPT, Perplexity, Gemini, etc., I’d separate the problem into two parts:

1. Create a strong, semantically complete outline.
2. Measure whether AI systems actually mention/cite the resulting content.

Those are not quite the same job.

My shortlist

Frase would be the first one I'd test if the primary requirement is "give me an outline designed around the questions and information an AI answer engine needs." Current comparisons describe it as particularly strong at AI content briefs, question research, and SERP-driven outlines, and it now includes GEO-oriented scoring/visibility functionality. Stork.AI FlowHunt

Surfer is worth considering if you want more sophisticated SEO/content optimization around the outline. Its January 2026 update specifically says its generated outlines are now more natural, shorter, citation-ready, and informed by brand, AI, and SEO context. Surfer SEO

MarketMuse becomes more interesting if you're not just making individual articles but want to determine which topics and subtopics your entire site should cover. Its strength is content inventory, topic gaps, and topical-authority planning rather than AI-answer tracking. Honest AI Guide

One important distinction

I wouldn't assume that an "AI-optimized" outline automatically makes an article more likely to be quoted.

For LLM citation/quotation, I'd structure your briefs to explicitly require:

• Direct answers near the top of each section
• Clear, self-contained statements that can be quoted without surrounding context
• Specific facts, numbers, definitions, and comparisons
• Primary/authoritative sources attached to important claims
• Original data, examples, or analysis rather than generic summaries
• Explicit answers to related questions
• Consistent terminology/entities throughout the article
• Tables or structured lists where appropriate
• Clear author/company expertise and attribution
• Minimal filler around the actual answer

Then use an AI-visibility/citation tracker after publishing to see whether ChatGPT, Perplexity, Gemini, etc. actually retrieve and cite those pages. Content-brief software and AI-citation monitoring are increasingly separate products; swapping one SEO brief tool for another doesn't necessarily give you the latter. 1001 SEO Media AI Search Tool Rank

If you tell me what kind of content you're producing (B2B SaaS, affiliate, ecommerce, publisher, local, etc.) and roughly how many articles/month, I can narrow this to 2–3 tools and give you a concrete "LLM citation-ready outline" workflow.

MarketMuse becomes more interesting if you're not just making individual articles but want to determine which topics and subtopics your entire site should cover. Its strength is content inventory, topic gaps, and topical-authority planning rather than AI-answer tracking.

One important distinction

I wouldn't assume that an "AI-optimized" outline automatically makes an article more likely to be quoted.

For LLM citation/quotation, I'd structure your briefs to explicitly require:

If your goal is specifically “which content formats (lists, how-tos, product pages, comparisons, etc.) are AI assistants citing most often?”, you’ll want an AI citation analytics / GEO (Generative Engine Optimization) tool that exposes cited URLs + page types + patterns, not just brand mentions. Linkeddit Scrunch

A few tools worth evaluating:

My recommendation by use case:

• You want a dashboard that tells you “AI assistants cite listicles 3× more than product pages in our category” → look at Profound or Scrunch AI first. They are closer to the raw citation data you need. Scrunch Nick Lafferty
• You want a lightweight experiment → use Otterly.ai or Peec AI, export cited URLs, then classify pages yourself (/blog/, /guides/, /products/, comparison pages, etc.). The Rank Masters Plug

A useful workflow is:

1. Track a fixed set of buyer prompts (e.g., “best X,” “how to choose X,” “X vs Y,” “X pricing”).
2. Export every cited URL from ChatGPT/Perplexity/Gemini/AI Overviews.
3. Classify each citation:

• listicle
• how-to/tutorial
• product page
• comparison page
• glossary/definition
• review/testimonial
• research/data page
• Calculate citation share by format.

Most tools today expose the citation URLs, but few automatically normalize them into “format performance” categories, so the last classification step is often custom. Scrunch

If you’re building a content strategy rather than just monitoring visibility, I’d prioritize a tool with URL-level citation exports over one that only gives an “AI visibility score.” Nick Lafferty

If your specific goal is “which content formats are AI assistants actually citing most?”, I’d start with HubSpot’s AI Search Sensor.

hubspot.com

It’s unusually well matched to your question because its Content Type view explicitly breaks AI citations down by formats such as blogs, comparison pages, documentation, guides, and others, and shows how citation share changes over time. Its data covers ChatGPT, Gemini, and Perplexity. HubSpot

What I’d use it for

• Format benchmarking: Are guides, listicles, comparisons, product pages, etc. disproportionately represented among citations?
• Trend detection: Is the mix changing week to week rather than relying on a static “AI likes X” claim?
• Source-type analysis: See which kinds of sources are being cited alongside content formats.
• Strategy decisions: Compare the citation patterns against your own site's content portfolio.

There’s also good independent evidence that this is the right level of analysis: HubSpot's 2026 AEO research found listicles, articles, product pages, and category pages among commonly cited formats, while comparison content performed particularly strongly for ChatGPT. HubSpot Blog

If you want something more granular for your own site, Microsoft Clarity's AI Citations dashboard is worth considering: it shows which individual pages are cited, citation counts, associated queries, and which content/themes perform well. learn.microsoft.com

So, in short: HubSpot AI Search Sensor for “what formats does AI cite?”; Clarity for “which of my pages get cited?”

Yes. The category you’re looking for is generally called AI visibility / LLM monitoring / AI search monitoring, but there’s an important distinction:

Most tools monitor the answers and citations rather than directly monitoring an assistant’s internal knowledge ingestion pipeline. You can use them to identify which third-party sources appear to be influencing how assistants describe your brand.

For example, otterly.ai runs your prompts across ChatGPT, Perplexity, Gemini, Claude, Copilot and Google AI surfaces and records the resulting brand mentions and cited URLs. Otterly Otterly

What you can actually monitor

A useful system would give you something like:

The particularly interesting part for your use case is the source layer. Recent monitoring research suggests that brand mentions can be relatively stable while the underlying citation sources change considerably, so simply tracking whether your brand appears isn't enough. Otterly

Tools worth looking at

• tryprofound.com — enterprise-oriented AI visibility/answer monitoring, including citations and source intelligence.
• otterly.ai — particularly straightforward for tracking prompts, mentions and the URLs AI engines cite.
• scrunchai.com — focuses heavily on AI crawler/citation intelligence and AI visibility.
• ahrefs.com — useful if your organization already has an SEO/competitive-intelligence stack.
• semrush.com — combines traditional search intelligence with AI visibility monitoring.

Current industry comparisons generally put Profound, Otterly, Scrunch, Ahrefs and Semrush among the major options in this space. TechnologyAdvice Sona

But there's a deeper product opportunity

If by "third-party knowledge sources being ingested" you literally mean:

"Tell me which external facts about my company are entering the information ecosystem that AI assistants may use, before they show up in an answer."

That's not quite the same product as AI visibility monitoring.

You'd want a source-to-answer observability layer:

Third-party web → source changes → AI retrieval/citation → assistant answer → brand-risk alert

For example:

Tuesday: G2 changes your company description
↓
Wednesday: that page starts appearing in AI citations
↓
Thursday: ChatGPT's answer about your product changes
↓
Alert: "Potential third-party source influence detected."

That would be substantially more useful than a dashboard saying "your AI visibility went down 4%."

One caveat: you generally cannot observe an assistant's private training data or proprietary retrieval/knowledge-ingestion pipeline directly. What you can observe is the external evidence layer and the assistant's outputs, then correlate changes between them.

If you're evaluating this for a brand/PR/SEO/enterprise monitoring product, I can also map out the existing vendors by what they actually monitor (sources vs citations vs answers vs crawlers) and identify the gap that isn't well served today.

If your goal is monthly client-facing LLM/GEO visibility reports with polished visual dashboards, I’d shortlist these:

My recommendation: AgencyAnalytics + AI Tracker

If you're a conventional digital/SEO agency and the deliverable is something like:

"Here is your monthly AI visibility report, alongside SEO, traffic, leads, PPC, etc."

I'd look very seriously at AgencyAnalytics.

Its AI Tracker covers ChatGPT, Google AI Overviews, Google AI Mode, Claude, Perplexity and Gemini, and—crucially—puts those metrics into the same dashboards/reports as your other marketing channels. It supports competitor comparisons, visibility trends, citations/sources, custom dashboards, automated reports and white-labeling. AgencyAnalytics

agencyanalytics.com

That makes it particularly good for selling LLM visibility as an additional monthly service rather than creating an entirely separate reporting ecosystem.

If you want a dedicated GEO/LLM reporting product: Peec

Peec AI is probably the one I'd demo first.

Its agency offering specifically supports branded Looker Studio dashboards, where clients can access dashboards without having a Peec login. It also provides CSV/API access for feeding data into BigQuery, Tableau, Power BI, etc. Peec AI

peec.ai

That's attractive if you want to build a dashboard that looks like:

Client → AI Visibility

• AI Share of Voice
• Brand Mention Rate
• Citation Rate
• Competitor Share of Voice
• Visibility by LLM
• Visibility by prompt category
• Sentiment / brand perception
• New citations
• Lost citations
• Top cited pages
• Competitors gaining visibility
• Month-over-month change
• Recommended actions

Then have the system automatically generate a monthly executive summary underneath it.

For enterprise clients: Profound

Profound is more appropriate if your clients are large brands and will expect fairly sophisticated analysis of what AI engines are saying about them.

It is positioned toward enterprise-grade analytics and agency use cases, including deeper prompt/answer analysis. The tradeoff is that it's generally a much heavier investment than the simpler monitoring platforms. AI Search Tools AxiomAI

tryprofound.com

What I'd choose by agency type

• SEO/PPC agency already using AgencyAnalytics: AgencyAnalytics AI Tracker
• Agency building a dedicated GEO/LLM service: Peec AI
• Small agency testing whether clients will pay for LLM visibility: Otterly.ai
• Enterprise GEO consulting: Profound
• Want visibility tracking + content/action workflows: Promptwatch

One important distinction: don't optimize for a dashboard that merely shows an "AI visibility score." For a monthly client service, the valuable report is the movement and explanation: Where did we gain/lose visibility? Which competitors are appearing instead? Which prompts matter? What sources are LLMs citing? What should we do next month? Current agency-focused comparisons similarly emphasize client-ready dashboards, exports, white-labeling and actionable gap analysis rather than just a score. The Rank Masters About Martech

If you tell me how many clients you have (e.g. 10, 50, 100+) and what you want to charge per client/month ($200, $500, $1k+), I can narrow this down to the best 2–3 options and map out the exact monthly dashboard/report I'd build.

agencyanalytics.com

That makes it particularly good for selling LLM visibility as an additional monthly service rather than creating an entirely separate reporting ecosystem.

If you want a dedicated GEO/LLM reporting product: Peec

Peec AI is probably the one I'd demo first.

Its agency offering specifically supports branded Looker Studio dashboards, where clients can access dashboards without having a Peec login. It also provides CSV/API access for feeding data into BigQuery, Tableau, Power BI, etc.

If you mean AI/LLM visibility reporting—e.g. “How often does our client appear in ChatGPT, Perplexity, Gemini, Claude, Google AI results, what sources are cited, and how are competitors performing?”—there are now several agency-focused platforms.

For a typical agency delivering a monthly client report + visual dashboard, I’d shortlist these:

My practical shortlist

1. LLM Pulse — if you want to plug into your existing reporting stack

Its agency offering specifically supports multi-client dashboards, white-labeling, and a Looker Studio connector, which is attractive if your agency already builds monthly reports in Looker Studio. It also lets you blend AI visibility with traditional SEO/PR metrics. LLM Pulse

llmpulse.ai

2. Tower GEO — if you want the reporting product basically ready-made

Tower is explicitly designed around agencies: multi-client workspaces, competitive benchmarking, sentiment/citation tracking, white-label PDFs, branded client dashboards, custom domains and automated monthly reports. Tower GEO

towergeo.com

3. GeoBubbles — if visual reporting is particularly important

Its agency product has a custom report builder, drag-and-drop cards, saved templates, shareable branded links, executive/C-level views, plus LLM citation tracking and geographic visualization. GeoBubbles

geobubbles.com

4. GetIntel — if the report needs to lead naturally into “what should we do?”

Beyond visibility reporting, GetIntel emphasizes identifying gaps and drafting fixes. It also offers a multi-client dashboard, white-label reports and a Looker Studio connector, so it can fit an existing agency reporting process. GetIntel

getintel.ai

What I'd put in the monthly dashboard

Regardless of vendor, I'd avoid making the report just a giant collection of LLM screenshots. A useful client dashboard would have:

• AI Visibility / Share of Voice
• Visibility trend vs. previous month
• Mention rate across tracked prompts
• Citation rate and top citation sources
• Average position/rank where measurable
• Competitor comparison
• Visibility by AI engine
• Visibility by topic / customer intent
• Newly gained and lost citations
• Prompts where competitors appear but the client doesn't
• Actual example AI answers
• Recommended actions for the next month
• “What changed?” executive summary

That last section is particularly important: clients generally care less about an abstract “LLM score” than what changed, why it changed, and what you're doing next.

There are also meaningful differences between white-label PDF reporting and a genuinely white-label client portal. Several vendors market both as “white label,” but they aren't equivalent; a client login on your domain is considerably more valuable if you're trying to productize LLM visibility as a recurring service. SEO for GPT

If I were evaluating them for an agency

I'd run a trial with LLM Pulse, Tower GEO, GeoBubbles, and GetIntel and give each the same 20–30 client prompts. I'd judge them on:

1. Data quality/repeatability of the LLM measurements.
2. How attractive the client dashboard looks without customization.
3. How much manual work it takes to produce the monthly report.
4. Whether clients can access a branded live portal, rather than just receive a PDF.
5. Competitor/citation analysis.
6. Ability to export/API the data into your existing reporting system.
7. Per-client economics at 10, 25, 50 and 100 clients.

The agency market is moving quickly, so I'd put considerably more weight on the actual client-facing workflow and unit economics than on whoever claims to have the highest “AI Visibility Score.” Current agency comparisons likewise emphasize multi-client workspaces, white-label reporting, scalable pricing and data depth as the key differentiators. The Rank Masters Maya

If you tell me roughly how many clients you want to report on (e.g. 10, 50, 200) and whether you want PDF reports, live client dashboards, or both, I can narrow this to the 2–3 platforms that fit and compare their current pricing/features.

2. Tower GEO — if you want the reporting product basically ready-made

Tower is explicitly designed around agencies: multi-client workspaces, competitive benchmarking, sentiment/citation tracking, white-label PDFs, branded client dashboards, custom domains and automated monthly reports. towergeo.com

towergeo.com

3. GeoBubbles — if visual reporting is particularly important

Its agency product has a custom report builder, drag-and-drop cards, saved templates, shareable branded links, executive/C-level views, plus LLM citation tracking and geographic visualization. geobubbles.com

geobubbles.com

4. GetIntel — if the report needs to lead naturally into “what should we do?”

Beyond visibility reporting, GetIntel emphasizes identifying gaps and drafting fixes. It also offers a multi-client dashboard, white-label reports and a Looker Studio connector, so it can fit an existing agency reporting process.