Answers that name a competitor

For a small ecommerce shop on a tight budget, I’d start with otterly.ai.

• $29/month for its Lite plan, with 15 prompts tracked daily. Otterly
• It monitors ChatGPT, Google AI Overviews, Perplexity, and Microsoft Copilot, so you can see whether your brand/products appear in answers and what gets cited. Otterly Otterly
• There’s a free trial with no credit card required, which makes it relatively low-risk to test. Otterly

One important ecommerce caveat

If your main question is specifically “Does AI recommend my individual product pages/SKUs?”, rather than just “Does AI mention my brand?”, peec.ai has more ecommerce-specific functionality. It supports product-catalog uploads, SKU-level tracking, shopping-query analysis, and visibility of the domains/URLs AI retrieves when recommending products. Peec AI

The tradeoff is price: Peec's Starter plan is currently listed at $95/month for 50 prompts and 3 models, so it's substantially more expensive. Peec AI

My budget approach: start with Otterly's 15 prompts and use them for your highest-value product/category queries—for example, “best _ for ,” “ alternatives,” and “where can I buy _?” If you eventually need SKU-level shopping visibility, that's when I'd consider moving to Peec.

If you tell me roughly how many products you sell and whether you're on Shopify, I can suggest a 15-prompt tracking setup that gets you the most useful signal for $29/month.

For a mid-market SaaS team, I’d shortlist Profound, Peec AI, and OtterlyAI. They all cover the core use case—competitor mentions/share of voice plus citation tracking—but their strengths differ.

What I'd look at closely

• Profound — particularly good if the question is “Which competitors are winning citations, on which prompts, and which pages/sources are responsible?” It tracks citation share, visibility rank, competitor changes, prompt-level gaps, and head-to-head content comparisons. Profound
• Peec AI — has explicit Share of Voice, visibility, position, competitor benchmarking, and source/citation analysis. Its citation gap analysis identifies sources where competitors are cited but you aren't, which is useful for turning the dashboard into an outreach/content list. Peec AI Peec AI
• OtterlyAI — especially interesting if you want straightforward ongoing monitoring. It tracks SOV versus competitors, individual cited URLs, citation frequency, and changes in citation position, with breakdowns by prompt, engine, and market. Otterly Otterly

For a SaaS team, I'd define the dashboard around 5 metrics

1. AI Share of Voice — your brand mentions divided by total tracked competitor mentions.
2. Brand coverage — % of relevant AI answers mentioning you at all.
3. Citation share — % of citations going to your domain versus competitors.
4. Citation gap — domains/URLs that cite competitors but not you.
5. Trend by engine + prompt — e.g. ChatGPT vs. Perplexity vs. Gemini, and “best X software,” “X alternatives,” “X for enterprise,” etc.

That distinction between coverage/SOV and citation share matters: you can be mentioned frequently while competitors still own the sources AI systems rely on. Peec and Profound both explicitly expose this distinction. Peec AI Profound

My practical starting point: demo Profound and Peec AI side-by-side, then use OtterlyAI as the comparison if budget/simplicity matters. I wouldn't choose based merely on number of AI engines; I'd test each against your actual 30–100 high-value SaaS prompts and see whether the competitor/citation data is actionable.

If you tell me roughly ARR/team size and how many competitors you want to track, I can also compare these three on pricing, prompt limits, API/reporting, engine coverage, and agency/team features.

For an enterprise needing historical, cross-model brand visibility analytics, I’d shortlist:

• tryprofound.com — strongest fit for enterprise-scale monitoring, broad model coverage, historical prompt/visibility analytics, citations, and competitor benchmarking. Baarely Marqeable
• business.adobe.com — compelling if you already use Adobe; combines AI visibility measurement with optimization and existing analytics workflows. Adobe for Business Experience League
• peec.ai — good analytics-focused alternative for multi-market, multi-engine tracking. Baarely

For your specific requirement, I’d start with Profound and Adobe in an enterprise bake-off. Make vendors demonstrate reproducible historical snapshots, model/version tracking, geographic/language segmentation, citation-level provenance, API/data export, SSO/RBAC, and retention—not just a current “visibility score.”

Yes. For a multi-language, multi-region site, I’d specifically look for a platform that tracks prompt × language × country × AI engine, and exposes the actual cited URLs, rather than giving you only a blended “AI visibility score.”

Based on current 2026 capabilities:

Otterly is particularly aligned with your use case: it explicitly advertises 65+ countries and languages, tracks every citation and cited URL, and lets you analyze performance market-by-market rather than collapsing everything into a global figure. Otterly Otterly

Profound is worth looking at if you're an enterprise and need higher-volume prompt monitoring and detailed citation analysis across many AI systems. Current comparisons document coverage across ChatGPT, Perplexity, Claude, Gemini, Grok, Copilot, DeepSeek and Google AI Overviews. Hiveminds

Peec AI is another good candidate if your emphasis is separating brand mentions from actual source citations and comparing markets/competitors. Hiveminds

What I'd test in a demo

For your situation, don't just ask “does it support multiple languages?” Ask the vendor to demonstrate something like:

Germany + German + ChatGPT + 50 prompts → which URLs are cited?

Then:

US + English + same prompts → how does the citation set change?

And ideally:

France + French + same intent → which competitors are cited instead of us?

You want to be able to see citation URL, cited domain, prompt, language, country, engine, date, competitor and historical change. The distinction between mentioned and actually cited is important—different tools can produce substantially different visibility numbers depending on how they measure it. Austin Heaton

If you tell me roughly how many languages/countries you operate in (e.g. 10 languages × 30 countries) and whether you're enterprise or SMB, I can narrow this to 2–3 platforms and compare their geographic/language tracking, citation depth, API/export capabilities, and likely cost.

For a growth marketer looking for AI share of voice (AI SoV) trends alongside traditional search metrics in one dashboard, the strongest options right now are:

My shortlist by use case

• B2B SaaS growth team: Semrush or Rankseer — easier to connect AI visibility changes with keyword/content campaigns. Rankseer Semrush
• Enterprise SEO department: BrightEdge or Pi Datametrics — stronger for large-scale competitive reporting and SERP intelligence. Pi Datametrics BrightEdge
• Early-stage company testing GEO/AEO: SERPrecon — more focused on AI search share-of-voice measurement. SERPrecon

A practical growth dashboard usually combines:

• AI SoV: % of tracked prompts where your brand appears vs competitors
• AI citations: which pages/sources AI engines reference
• SEO visibility: rankings, impressions, clicks, estimated traffic
• Content impact: pages published/updated vs visibility movement

One caveat: AI SoV is still a developing metric. Different vendors use different prompt sets, models, sampling frequency, and formulas, so comparisons are most meaningful within the same tool over time rather than across vendors. Semrush GeoEcho

If I were building a growth marketer’s single dashboard today, I’d start with Semrush + an AI visibility module for broad SEO coverage, or Rankseer/SERPrecon if the main goal is optimizing for AI search discovery. SERPrecon Rankseer Semrush

For AI crawler + content-level attribution, I’d use a layered stack:

• Ahrefs Bot Analytics — strongest straightforward option for identifying AI crawlers and exact URLs crawled (GPTBot, ClaudeBot, PerplexityBot, etc.). Ahrefs Help Center Ahrefs
• Botify — stronger enterprise option if you want to connect AI crawler behavior, individual URLs, visibility in AI answers, traffic, and revenue/engagement data. Botify Knowledge Base Botify Knowledge Base
• GA4 — best for human referral attribution: which content pieces actually receive clicks from ChatGPT/Gemini/etc. It still has blind spots, particularly AI-influenced visits and Google AI Overviews. Cituna Nice Looking Data
• Server/CDN logs (e.g., Cloudflare) — essential for the raw crawler layer because JavaScript analytics won't see AI bots. Ahrefs

My architecture: CDN/server logs → AI crawler → URL + GA4 → AI referral → landing URL → conversion + Botify/Ahrefs → AI visibility/citations.

If your core question is “which articles are being crawled/cited by AI and which subsequently generate pipeline?”, Botify + server logs + GA4 is the most complete setup. Botify Knowledge Base Botify Knowledge Base

For a small content agency, you’re looking for an AI visibility / AEO (Answer Engine Optimization) monitoring tool rather than a traditional SEO rank tracker. The key capabilities to look for are:

• Run a set of client-specific prompts repeatedly (“best CRM for startups,” “top accountants in Chicago,” etc.)
• Check whether the client appears in answers from ChatGPT, Claude, Gemini, Perplexity, etc.
• Capture the exact answer text
• Extract citations/sources/URLs used by the AI assistant
• Compare against competitors
• Produce client-facing reports

Some tools worth evaluating:

For a small content agency, I’d usually shortlist:

1. MentionScout — if you need agency-style dashboards and recurring client reporting. MentionScout
2. Citations.io — if your main deliverable is “here are the sources AI trusts and what we should create next.” Citations
3. CitePrism — if citation tracking is the core metric you want to sell clients. CitePrism

A practical agency workflow:

• Create 20–50 “buyer prompts” per client
• Track weekly:

• % of prompts where client appears
• % where client is cited
• citation URLs (their site, reviews, directories, publications)
• competitors appearing instead
• changes after publishing new content
• Turn that into a monthly “AI search visibility” report

One thing to watch: these tools measure sampled prompts, not every possible AI answer. The quality of your prompt set matters as much as the tool.

If you tell me your agency size (e.g., 5 clients vs 100 clients), client type (local businesses, SaaS, ecommerce, B2B), and whether you need white-label reports, I can narrow this to the best fit.

Yes. There’s now a small but growing category of AI citation / AI visibility monitoring tools that can do something close to what you’re describing: monitor ChatGPT, Perplexity, Gemini, Google AI Overviews, etc., identify the URLs they cite, compare those citations with competitors, and in some cases alert you when visibility/citations change.

A few worth looking at:

• ahrefs.com — tracks AI mentions/citations across major AI platforms, including the specific domains and pages being cited, plus competitor benchmarking. It also has daily monitoring. Ahrefs Help Center Ahrefs
• getveritas.io — specifically tracks which of your pages and competitor pages are being cited for monitored prompts, with ongoing citation monitoring. Veritas
• mentionsflow.com — tracks citations across ChatGPT, Claude, Gemini, Perplexity and Google AI Overviews, including the source/page leaderboard and alerts when visibility changes. Mentions Flow
• aicitationmonitor.com — monitors citations and the exact sources AI engines use, including competitor citation share, with alerts when things shift. AI Citation Monitor
• citedspy.com — scheduled monitoring across ChatGPT, Perplexity, Gemini, Copilot and Google AI Mode, including the pages being cited and competitor share of voice. CitedSpy

One important distinction

If by "alerts when AI assistants start citing a new page from our domain" you literally mean:

"Our /blog/new-page was never cited by ChatGPT/Perplexity/etc., and today it was cited for the first time. Send Slack/email."

That's a more specific requirement than ordinary "AI visibility monitoring."

Most tools work by re-running a defined set of prompts periodically and recording which sources appear. So you can derive a "new citation" alert from the historical data, but it's not necessarily a firehose of every AI-generated answer on the internet.

For example, MentionsFlow explicitly offers scheduled monitoring plus alerts, while Ahrefs' Brand Radar is more oriented toward large-scale AI visibility/citation datasets. Mentions Flow Ahrefs Help Center

If what you're envisioning is "Google Alerts, but for AI citations—with our domain + 10 competitors, URL-level detection, and Slack alerts", I can also look specifically for tools that support that exact workflow and compare pricing, engines covered, alert granularity, API/Slack support, and whether they can monitor arbitrary competitor domains.

For enterprise marketing teams looking to report AI answer rankings and historical trends across multiple AI models, the category you’re looking for is usually called AI Visibility / Answer Engine Optimization (AEO) / Generative Engine Optimization (GEO) platforms. These tools track how often a brand appears in AI-generated answers, where it ranks in recommendations, which sources are cited, and how visibility changes over time. visibility.sowww.withsurface.com

Some enterprise-oriented options:

For a large enterprise marketing reporting use case, I’d evaluate platforms based on these capabilities:

1. Prompt-level tracking
   • Example: “best CRM for mid-market companies”
   • See whether your brand appears, where it appears in the answer, and which competitors are mentioned.

2. Model-by-model dashboards
   • ChatGPT vs Gemini vs Claude vs Perplexity can produce different recommendations, so enterprise reporting should avoid collapsing everything into a single score. visibility.sowww.withsurface.com

3. Historical reporting
   • Weekly/monthly trend lines showing:
     • AI answer share of voice
     • average position/rank
     • mentions
     • citations
     • competitor movement visibility.sowww.getmentions.aiairpulse.ai

4. Executive reporting
   • Exportable dashboards or APIs for connecting AI visibility metrics to existing marketing intelligence systems. Some platforms expose metrics such as share of answer, average position, mentions, citations, and historical series through APIs. prominenceai.io

If you mean enterprise-scale (Fortune 500 / global marketing ops) rather than a smaller SEO team, the shortlist I’d start with is Profound, BrightEdge, AirPulse, and enterprise tiers of AI visibility platforms like GetMentions or Visibility.so—then compare model coverage, API access, governance, and reporting workflows. www.getmentions.aiairpulse.ai

If you mean a tool that connects to your existing analytics stack (like GA4) and attributes conversions that came from AI assistant referrals (ChatGPT, Claude, Gemini, Perplexity, etc.), several tools are emerging:

• Centium — connects with Google Analytics and identifies AI-referred sessions, then attributes sessions, purchases, and revenue back to AI sources/models. Centium
• Goodie — focuses on AI search attribution, connecting AI visibility with conversion funnels and revenue measurement. higoodie
• Convultra — markets AI attribution for traffic from ChatGPT, Claude, Perplexity, and similar assistants, alongside broader conversion tracking. Convultra

If you mean attributing conversions inside an existing marketing attribution platform rather than only tracking AI referrals, tools like Attribution can connect attribution data to AI assistants through MCP so an assistant can query conversion paths, revenue, and channel performance. Attribution

A practical setup is usually:

1. GA4 or your product analytics tool → captures visits, events, purchases.
2. AI referral attribution layer → identifies AI-originated traffic and maps it to models/sources.
3. CRM or revenue system → connects AI-influenced visits to leads and closed revenue.

The right choice depends on whether you want:

• click-through attribution (someone clicks a link in an AI answer and converts), or
• influence attribution (AI mentioned/recommended your brand, even if the user later converts through another channel). higoodie

If you tell me your current stack (GA4, Segment, HubSpot, Shopify, Amplitude, Mixpanel, etc.), I can narrow it down.

For a small ecommerce team, I’d look at these first:

• brandecho.app — especially interesting if you’re on Shopify. It tracks how products/brands appear in ChatGPT, Claude, Perplexity, and Gemini, including mentions and share of voice. It’s positioned specifically for Shopify merchants. BrandEcho
• otterly.ai — a relatively straightforward AI-search visibility monitor for tracking whether your brand appears in AI answers and how competitors compare. Current industry comparisons generally position it toward smaller teams and lower-cost monitoring. Toolsolved Bloomiro
• semrush.com — worth considering if you already use Semrush. Its ecommerce offering combines traditional SEO with AI visibility, including product/brand mentions, AI share of voice, competitors, and citation analysis. Semrush
• authoritas.com — more ecommerce-specific: you can import product keywords/catalog data and measure branded vs. unbranded AI visibility, share of voice, competitor products, and which third-party pages AI cites. Authoritas
• yotpo.com — particularly relevant if you have a sizable product catalog. It syncs the catalog and measures AI share of voice by product, category, persona, and buying stage. Yotpo

What I'd measure

Don't just track "does ChatGPT mention us?" I'd set up a dashboard around:

1. Mention rate — percentage of relevant buyer prompts where your product/brand appears.
2. AI share of voice — your mentions versus competitors.
3. Position — where your product appears in the recommendation.
4. Citation rate — how often AI assistants cite/link your product pages.
5. Product-level visibility — which SKUs/categories are being surfaced or ignored.
6. Prompt-level visibility — e.g. "best running shoes under $150," "Brand A vs Brand B," "best gifts for…"
7. Trend over time — whether page/catalog changes actually increase AI visibility.

For a small team, I'd start with BrandEcho if you're Shopify-based, or Otterly if you want a more general AI-visibility tracker. If you already pay for Semrush, its AI visibility functionality is worth evaluating before adding another platform. Semrush BrandEcho

If you tell me your ecommerce platform (Shopify/WooCommerce/etc.), approximate number of products, and monthly budget, I can narrow this to 2–3 tools and compare their AI engines, product-page tracking, share-of-voice metrics, and pricing.

If your agency’s specific goal is “Which of our clients’ blog articles are being cited or used as sources inside ChatGPT, Perplexity, Gemini, Google AI Overviews/AI Mode, etc.?”, I’d evaluate these a little differently from ordinary “AI visibility” software.

My shortlist

Recent 2026 comparisons consistently identify Profound, Peec, Otterly, Scrunch, Ahrefs and Semrush as the major options in this category, although capabilities and pricing are changing quickly. Baarely UX Continuum

For your use case, I'd look hardest at Peec and Profound

Peec AI is particularly interesting for an agency because it separates brand mentions from source citations, which is important for what you're asking. You don't just want to know “Client X appeared in 27% of answers.” You want to know:

“The client's article /blog/best-crm-for-small-business/ was cited in 14 answers, across these prompts and these engines.”

That distinction is critical for demonstrating the value of your content program. Recent comparisons specifically call out Peec's citation/mention separation and agency-oriented reporting capabilities. StayFound

Profound is the one I'd investigate if your clients are larger and you're willing to pay for significantly deeper data. Its higher tiers cover a broad range of answer engines and provide substantially more enterprise-oriented analytics. Baarely

One important distinction

There are actually three different metrics that vendors sometimes lump together as “AI visibility”:

1. Brand mention — “ChatGPT mentioned Acme.”
2. Citation/source — “ChatGPT cited Acme's specific blog URL.”
3. AI-attributed traffic — “An AI engine actually sent a visitor to that URL.”

For a content marketing agency, #2 is probably your most interesting KPI, while #3 is the eventual business KPI.

I'd therefore ask every vendor to demonstrate this exact workflow before buying:

Give the system 100–300 real buyer questions → run them against ChatGPT/Perplexity/Gemini/Google AI → show every citation → map citations back to individual URLs on our clients' domains → show changes over time → export that data per client.

If a product primarily gives you mention rate / share of voice, but can't reliably expose the specific cited URL, I'd consider that a different product from what you're looking for.

What I'd want in an agency stack

For each client, ideally you can produce something like:

• 1,000 tracked buyer prompts
• AI visibility by engine
• Citation rate
• Citations by URL
• New/lost citations month-over-month
• Which competitors' URLs are being cited instead
• Citation rate by content cluster
• Questions where your client has zero citations
• Questions where the client is mentioned but another website supplies the citation
• AI crawler activity on the client's site
• Historical snapshots of actual AI answers
• Client-specific dashboards
• White-label PDF/Looker Studio/API reporting

That last-but-one point matters: an answer saying your client is an authority is not the same as an AI engine actually citing your client's content.

There is also growing evidence that editorial/third-party sources are a major component of AI references, so I'd avoid restricting your monitoring to your own domain. vogue.com

My practical recommendation

If I were building this into a marketing-agency service, I'd demo Peec AI and Profound first, then use Otterly as the lower-cost benchmark.

I'd specifically compare them on URL-level citation extraction, not their headline “AI visibility score.”

If you tell me roughly how many clients you manage (e.g. 10, 50, 200), monthly budget, and which engines matter most, I can do a much more useful agency-specific comparison of Peec vs Profound vs Otterly vs Ahrefs/Semrush, including current pricing, API/export capabilities, white-label reporting, and exactly how well each handles blog-URL citation tracking. Baarely bloomiro.com

My practical recommendation

If I were building this into a marketing-agency service, I'd demo Peec AI and Profound first, then use Otterly as the lower-cost benchmark.

I'd specifically compare them on URL-level citation extraction, not their headline “AI visibility score.”

If your goal is specifically recommendations for improving structured data, metadata, and content so your site is more likely to be understood and cited in AI-generated answers, I’d look at Semrush’s AI Visibility Toolkit or HubSpot AEO rather than a schema validator alone.

What I’d use

• Semrush AI Visibility Toolkit — good if you want your existing SEO workflow plus AI-search analysis. It can track AI visibility and citations and provide AI-readiness insights, making it useful for identifying pages/content that need improvement. HubSpot Timothe AI
• HubSpot AEO — particularly useful if you want prioritized recommendations rather than just monitoring. Its AEO tooling includes prompt tracking, citation analysis, and content recommendations. HubSpot
• Google Rich Results Test + Schema Markup Validator — use these alongside either platform to validate the actual JSON-LD/Schema.org implementation. Google's Rich Results Test checks which Google rich-result types your structured data can support. Google

One important distinction: structured data is helpful for machine understanding, but it isn't a guaranteed mechanism for getting cited by generative AI. Current AEO guidance treats question coverage, clear answer-oriented content, entity consistency, crawlability, citations/authority, and structured data as parts of a broader optimization process. Timothe AI LovedByAI

If you want one tool specifically for “tell me what metadata/schema/content changes I should make on this page,” I'd start with HubSpot AEO. If you want, I can also give you a shortlist of 5 tools specifically focused on automated Schema.org/JSON-LD + AI-search recommendations, with pricing and capabilities compared.

Yes. A new category of tools—often called AI visibility tracking, Generative Engine Optimization (GEO) monitoring, or AI search analytics—is emerging specifically for this. They generally do not have access to a private feed of all ChatGPT conversations; instead, they run a controlled set of customer-relevant prompts against AI systems and measure how often your product appears, where it appears, what sources are cited, and the surrounding context. Rovoki

Some platforms to look at:

The metrics you probably want for a product are:

• Mention rate — “When users ask best X products, how often does my product appear?”
• Context classification — Is it:

• recommended?
• compared?
• mentioned as an alternative?
• criticized?
• incorrectly described?
• Share of voice — How often do you appear versus competitors for the same prompts.
• Citation sources — Which websites, reviews, forums, or documentation pages are influencing the AI answer.
• Prompt coverage — Which buyer questions lead to your product appearing (or not appearing).
• Position in answer — Whether you are the first recommendation or buried later. Zumi

For a product team, I would set up a prompt library rather than just tracking your brand name. For example:

• “What are the best [category] products for [use case]?”
• “Compare [your product] vs [competitor].”
• “What should a [persona] buy if they need [problem solved]?”
• “What are alternatives to [leading competitor]?”

Then track those prompts weekly or daily and review the actual AI responses. Ansvisor

One caveat: these tools measure sampled AI outputs, not every real user conversation. AI responses vary by model, date, location, user context, and whether live search is enabled, so the numbers are best treated as an AI visibility benchmark rather than a complete census. Rovoki

If you tell me your product category (e.g., SaaS, ecommerce, consumer hardware, marketplace, app), I can suggest which of these tools is the closest fit.

If you mean “which of my actual product pages/SKUs show up in AI shopping answers, and what position do they occupy?”, there are now tools built specifically for that.

• tryprofound.com — tracks product placement inside ChatGPT Shopping, including SKU-level analysis, queries that trigger shopping results, competitors, and which products rank ahead of yours. Profound
• yotpo.com — particularly ecommerce-focused: tracks individual products across ChatGPT, Gemini and other AI engines, including where each product appears, by product/category/region. Yotpo
• semrush.com — broader SEO + AI visibility platform; tracks how ChatGPT, Perplexity and Gemini describe/recommend your products and provides competitor/share-of-voice analysis. Semrush
• searchpanel.ai — more lightweight/product-level approach; you can analyze a product URL/SKU and see its visibility across multiple AI engines. Searchpanel

For your exact use case

I'd distinguish between brand-level AI visibility and product-level AI ranking.

If your question is:

“When someone asks ChatGPT ‘What are the best running shoes under $150?’, does my /products/xyz page appear, and is it #1, #3, #7, etc.?”

then Profound and Yotpo Discover are the closest matches based on their current documented capabilities. Profound is especially explicit about placement within ChatGPT Shopping; Yotpo emphasizes tracking each product across multiple AI engines. Profound Yotpo

If you're building an AI-search analytics stack for an ecommerce store, the metrics I'd want are:

1. Product/SKU appearance rate — percentage of relevant prompts where the product appears.
2. Average position — where it appears when it does.
3. Engine-by-engine visibility — ChatGPT vs. Gemini vs. Perplexity, etc.
4. Competitor displacement — which products appear above yours.
5. Citation/source data — which websites AI is using to justify the recommendation.
6. Prompt-level history — the actual shopping questions and responses over time.
7. Product-page attribution — whether the AI result points to your PDP versus Amazon, a retailer, or another merchant.

That last distinction is important: being mentioned isn't necessarily the same as your product page being returned as the shopping destination. Profound explicitly tracks merchant/checkout ownership in ChatGPT Shopping. Profound

If you tell me whether you're looking for a tool to buy/use or an API you could integrate into your own ecommerce analytics product, I can narrow this down considerably.

If you’re looking to measure AI assistant share of voice (SoV) for specific keywords, prompts, and brand terms, you’re looking for an AI visibility / GEO (Generative Engine Optimization) tracking tool rather than a traditional SEO rank tracker.

Here are some tools worth evaluating:

My shortlist by use case

• Enterprise brand team / CMO reporting: Profound, Scrunch AI, AthenaHQ
• SEO team adding AI search tracking: Peec AI, Rankscale, SOV Tracker
• Need a simple “what % of AI answers mention us vs competitors?” metric: SourceWatch, CitedSpy, SOV Tracker SourceWatch CitedSpy SOV Tracker

Features I’d require before buying

A good AI SoV tool should let you:

• Define a fixed set of buyer prompts (not just keywords)
• Track exact brand terms and competitors
• Measure:

• % of answers mentioning your brand
• share of mentions vs competitors
• citation frequency and cited URLs
• position/order of appearance in answers
• sentiment/context of mentions
• Break results down by:

• ChatGPT
• Gemini
• Claude
• Perplexity
• Google AI features
• Export historical reports

One important distinction: AI assistants don’t really have “keyword rankings” like Google. The better metric is usually “when users ask these category questions, how often does the assistant mention or recommend my brand compared with alternatives?” Verity Score Foglift

If you share your category (e.g., SaaS, ecommerce, healthcare, finance) and whether you need enterprise reporting or a lightweight tracker, I can narrow this to 2–3 options.

Yes. What you’re describing is generally called AI visibility / LLM visibility / GEO tracking.

Tools that specifically break down brand appearances by AI platform include:

• Ahrefs Brand Radar — tracks mentions/citations across ChatGPT, Gemini, Perplexity, Copilot, and Google AI Overviews. Ahrefs
• SearchPrism — particularly relevant if you want an appearance matrix by AI engine, including ChatGPT, Claude, Gemini, and Perplexity. Prism
• Keysonar — provides per-model breakdowns of mentions, citations, and positions. Keysonar
• Renown — explicitly provides model-by-model visibility scores and trends. tryrenown.com
• VisiMatrix — tracks multiple providers and breaks down mention rate/visibility by provider. visimatrix.com

If by “different models and versions” you mean something like GPT-5 vs GPT-5.1 vs GPT-5.2, Claude Sonnet vs Opus, Gemini versions, rather than simply ChatGPT vs Claude vs Gemini, that's a more specific requirement. I can identify which tools actually expose model/version-level distribution rather than just platform-level reporting.

If your specific problem is “tell me when an AI answer says something negative or factually wrong about our brand, and show me why”, I’d focus less on generic brand monitoring and more on AI-search/answer monitoring with claim-level accuracy tracking.

The main options

My take for your use case

Profound is the one I would investigate first if inaccurate AI claims are the central problem. Its Answer Engine Insights specifically includes an “Ensure AI Accuracy” capability and a FactCheck feature designed to identify inaccurate claims and their sources. Profound Profound Help Center

Peec AI is particularly interesting if you want to understand how AI perceives your brand. It tracks sentiment, attributes associated with the brand, mentions, and the sources behind AI answers across ChatGPT, Gemini, Perplexity, Google AI experiences, and Claude. Peec AI Peec AI

OtterlyAI is a good option if breadth and day-to-day monitoring matter most. It tracks prompts across ChatGPT, Perplexity, Gemini, Google AI Overviews/AI Mode, Copilot and Claude, and records mentions, sentiment, citations and competitors. Otterly Otterly

The important distinction is that sentiment ≠ accuracy. A tool can tell you that an answer is negative, but the more valuable workflow for reputation teams is:

AI answer → problematic claim → cited/source material → evidence that the claim is wrong → alert → remediation

That's why I'd put extra weight on claim/source tracing rather than simply looking for a negative sentiment score.

What I'd want in a buying evaluation

Run the same 30–50 prompts through each vendor, including questions such as:

• “What are the biggest problems with [Brand]?”
• “Is [Brand] reliable?”
• “What complaints do customers have about [Brand]?”
• “[Brand] vs [Competitor]”
• “Has [Brand] ever [specific potentially false claim]?”
• “Is [Brand] a good choice for [use case]?”
• Questions containing known misinformation you've previously encountered.

Then measure:

1. False-negative detection — does it catch genuinely problematic answers?
2. False-positive rate — does it incorrectly flag ordinary statements as negative/inaccurate?
3. Claim-level evidence — can you see the exact problematic assertion?
4. Source attribution — can you identify the web pages apparently feeding the answer?
5. Engine coverage — ChatGPT, Gemini, Perplexity, Claude, Google AI experiences, etc.
6. Prompt coverage — can you build a realistic corpus of questions customers actually ask?
7. Historical tracking — can you see whether the misinformation is spreading or disappearing?
8. Alerts/API — can PR, legal, communications, or support teams automatically receive material changes?

One other consideration: AI answers can vary substantially by engine and prompt. Peec, for example, explicitly notes that different engines can surface different competitor sets and sources. Peec AI

So if you tell me your company size (startup/mid-market/enterprise), approximate number of brands/products, and which AI systems matter most (ChatGPT only vs ChatGPT + Gemini + Perplexity + Google), I can give you a neutral feature-by-feature comparison of Profound vs Peec vs Otterly and identify the capabilities you'd want to test in a proof of concept.

The important distinction is that sentiment ≠ accuracy. A tool can tell you that an answer is negative, but the more valuable workflow for reputation teams is:

AI answer → problematic claim → cited/source material → evidence that the claim is wrong → alert → remediation

That's why I'd put extra weight on claim/source tracing rather than simply looking for a negative sentiment score.

What I'd want in a buying evaluation

If by “monitoring client brand mentions inside conversational AI” you mean tracking how often clients are mentioned/recommended in ChatGPT, Perplexity, Gemini, Google AI Overviews, etc., there are a few good options.

My shortlist for a small agency

OtterlyAI is probably where I'd start if budget is the main constraint. Its entry tier is listed at $29/month for 15 prompts, and it monitors mentions/rankings, sentiment and links across ChatGPT, Perplexity and Google AI experiences. Its current analytics product also covers Gemini, Copilot and Claude. G2 Otterly Otterly

Peec AI becomes interesting if you're actually selling this as an agency service. Its agency plans are designed around multiple client projects and include client seats/reporting; the current Essential agency tier is listed at $245/month with roughly 111 prompts and 3 projects. Peec AI Peec AI

Profound is more expensive once you need meaningful volume, but its $99 Starter plan gives you 50 prompts and ChatGPT tracking. Its Growth plan is $399/month and expands to three answer engines and 100 prompts. Profound

What I'd do in your position

If you have 1–3 clients you're testing this with, I'd use OtterlyAI and create ~10–15 high-value prompts per client, rather than trying to monitor hundreds of generic queries.

For each client, track things like:

• “best [category] companies for [use case]”
• “[category] alternatives to [competitor]”
• “who should I use for [specific problem]?”
• “[client] reviews”
• “[client] vs [competitor]”
• “is [client] worth it?”
• “[category] companies in [location]”
• prompts reflecting the client's actual customer personas

That gives you a much more useful AI visibility/brand-mention report than simply counting whether the client's name appeared.

If you expect to turn this into a recurring agency deliverable across 5–10+ clients, I'd look much harder at Peec because its agency packaging and client reporting are designed for that use case. Peec AI Peec AI

One important distinction: these products generally measure AI-search visibility—whether and how a brand appears in generated answers—not every private conversation somebody has with an LLM. So “monitoring client mentions in conversational AI” is best thought of as controlled prompt monitoring, not social-listening-style surveillance of ChatGPT conversations. Peec AI

If you tell me roughly how many clients you have (e.g. 3, 10, 25) and your monthly budget ($50 / $200 / $500), I can narrow this down to the most economical setup.

Yes. If by AI Share of Voice (SOV) you mean “for a defined set of brand/category/buyer queries, how often does my brand appear in ChatGPT/Perplexity/Gemini/etc. compared with competitors?”, there are several platforms built specifically for this.

Platforms worth looking at

• ahrefs.com — Tracks custom buyer prompts, AI mentions, citations, and competitor visibility. Particularly useful if you also want traditional SEO/search data in the same ecosystem. It supports benchmarking brands across AI search and can generate prompts from competitor comparisons.
• tryprofound.com — Enterprise-oriented AI-search/GEO monitoring. Strong fit if you want large-scale prompt tracking and competitive AI visibility reporting.
• zumihq.com — Very close to the specific use case you described: define your brand + competitors + buyer questions, then track mention rate, SOV, average position, citations, and individual AI engines. Zumi
• rankvia.ai — Explicitly breaks visibility down by buyer question, competitor, AI platform, and source, including competitor SOV. This is useful if you want to go from an aggregate SOV number down to which queries are responsible for the gap. Rankvia
• measurellm.com — Tracks prompts across eight AI platforms and provides competitive SOV and visibility analysis. MeasureLLM
• docs.llmmonitor.io — Tracks ChatGPT, Gemini, Claude and Perplexity, with competitor benchmarking for SOV, position and sentiment. LLMMonitor
• airtrace.app — More lightweight/monitoring-oriented. Its dashboard combines SOV, provider breakdown, competitor rankings, citations and the actual AI responses behind the measurements. Airtrace
• serprecon.com — Interesting if you want AI SOV + traditional SEO SOV together. It tracks Google, ChatGPT and Perplexity and lets you break SOV down by keyword/domain. SERPrecon

For your exact requirement

I'd distinguish the platforms this way:

One important distinction: “AI visibility” isn't necessarily “AI Share of Voice.” Some tools primarily tell you whether your brand appears. For competitive SOV, you want a system that calculates the brand's proportion of mentions/appearances across the same controlled query set, and lets you drill down by query × competitor × AI engine × time. Zumi, Rankvia and MeasureLLM explicitly describe that workflow. Zumi Rankvia MeasureLLM

If you're evaluating these for a marketing/brand team, I can also give you a shortlist of 5 platforms specifically for “brand query + competitor comparison”, with pricing, engines covered, query-level reporting, API access, and whether they support custom prompts.

If the key requirement is “show me the AI answer, the URL that was/wasn’t cited, and then tell me what SEO/content issue on my site explains the gap,” I’d look closely at Scrunch AI.

Its Site Diagnostics product is unusually close to that workflow: it evaluates individual pages, identifies AI-search blockers, and produces specific content recommendations rather than stopping at an AI-visibility score. Scrunch

How I’d shortlist them

• Scrunch AI — strongest fit if you want AI occurrence → cited/source pages → page-level diagnosis → recommended fix. Its diagnostics explicitly identify what to fix on individual pages. Scrunch
• OtterlyAI — good if you want a broader monitoring platform. It tracks individual URLs cited in AI answers and combines that with crawlability checks, content audits, and prioritized GEO recommendations. Otterly
• Peec AI — particularly useful for connecting specific AI prompts and citations to recommended content actions; it says its recommendations identify what content to create or optimize. Peec AI Peec AI
• Profound — worth considering if you're an enterprise SEO organization and need deep citation/LLM monitoring, but I'd validate how much of the workflow actually gets down to “this URL has this specific content problem.” Current comparisons describe it as particularly strong on enterprise-scale visibility and citation data. Omnicite PickMySoft

My practical recommendation: demo Scrunch and Otterly side-by-side, using 20–50 of your actual target questions. Don't just ask whether they detect mentions. Ask each vendor to demonstrate:

AI prompt → exact response → cited URLs → your relevant URL → missing/weak content element → recommended change → ability to measure whether the change improved citation frequency

That last chain is the differentiator. A lot of AEO platforms are still essentially visibility dashboards, rather than systems that close the loop back to an actionable SEO issue. noveltyseo.com promptwatch.com

If you tell me your site size, CMS, and whether you care primarily about ChatGPT/Perplexity/Google AI Overviews, I can narrow this to 2–3 platforms and compare their actual workflows, integrations, and pricing.

If your core question is “How often does our product get mentioned/recommended/cited in AI answers vs. competitors?”, you’re looking for an AI search visibility / AEO / GEO monitoring platform. These tools run a fixed set of buyer prompts across AI engines and measure mentions, citations, competitor presence, and share of voice. SaaSworthy TechnologyAdvice

The shortlist I'd investigate

• tryprofound.com — Strong fit for larger companies. It focuses heavily on enterprise AI-search analytics, prompt tracking, competitor benchmarking, and source/citation intelligence. Geohats SEO Counselors
• peec.ai — Good middle ground for marketing/SEO teams wanting ongoing tracking of brand visibility, competitors, prompts and citations without going immediately to an enterprise platform. Geohats Conbersa
• otterly.ai — Worth looking at if you primarily need straightforward, relatively inexpensive monitoring across multiple AI search engines. Geohats TechnologyAdvice
• semrush.com — Particularly logical if your team already uses Semrush, since its AI Toolkit adds AI-visibility tracking alongside traditional SEO data. SaaSworthy
• ahrefs.com — Its Brand Radar is interesting if you want AI visibility alongside your existing SEO/brand research workflow. SaaSworthy
• scrunchai.com — Another option centered specifically on generative-engine optimization and monitoring. SaaSworthy TechnologyAdvice

What I'd measure

Don't settle for a single “AI visibility score.” The useful dashboard should separately show:

That distinction between being mentioned and being cited is particularly important: a product can have high mention visibility while generating relatively little attributable traffic because the AI isn't using its site as a source. Austin Heaton

For your specific use case, I'd demo Profound, Peec AI, and Otterly side-by-side using exactly the same 50–100 buyer prompts and competitors. The most important thing to test is whether you can inspect the actual underlying AI responses, rather than being handed only an opaque visibility score. Independent comparisons note that tools can produce different measurements from the same prompts, so methodology and auditability matter. Austin Heaton getmentioned.co

If you tell me your product category + 3–5 main competitors + approximate company size, I can narrow this to the 2–3 tools that fit your situation and compare their current pricing, AI-engine coverage, competitor tracking, API/export capabilities, and prompt limits.

Yes. A new category of tools is emerging around AI visibility tracking, Answer Engine Optimization (AEO), Generative Engine Optimization (GEO), or LLM visibility monitoring. They are designed for enterprise marketing, SEO, content, and competitive intelligence teams that want to know:

• When and where their brand appears in AI answers
• Which competitors are mentioned instead
• Which prompts drive visibility gaps
• Which sources/citations influence AI responses
• How visibility changes over time across ChatGPT, Gemini, Perplexity, Claude, and AI search experiences

Some tools in this space:

For an enterprise evaluation, I’d look for these capabilities:

Core measurement

• Prompt library management — define the questions your buyers actually ask
• Repeatable answer capture — avoid relying on one-off AI responses
• Share of voice — percentage of tracked answers where you appear vs. competitors
• Position tracking — e.g., whether you are listed first, recommended, or only mentioned
• Citation analysis — which websites, reviews, communities, and documents AI systems rely on

Competitive intelligence

• Competitor discovery (who AI recommends that you did not expect)
• Prompt-by-prompt competitor comparisons
• “Lost visibility” alerts
• Citation gap analysis (competitor sources you lack)

Enterprise workflow

• Multi-brand / multi-region support
• Team permissions
• Scheduled reporting
• API access
• Exportable dashboards for executives

The market is still developing, so I’d be cautious about treating any single “AI visibility score” as equivalent to Google rankings. The most useful platforms tend to expose the underlying evidence: the prompts run, the answers returned, the competitors mentioned, and the citations behind those answers. geolyze.org

If you’re evaluating this for a large B2B company, SaaS company, agency, or SEO/content team, the shortlist would likely differ.

Yes. For long-tail SaaS queries specifically, I’d look at tools that let you supply your own prompt set rather than relying only on generic keyword discovery.

• tryprofound.com — particularly strong for this use case. It runs tracked prompts daily and reports Share of Voice at the individual-prompt level, plus visibility, position, citations, and competitor data. Its prompt table also supports prompt-level analysis and query fan-outs. Profound Profound Help Center
• peec.ai — good fit for SaaS teams wanting to build a custom prompt library around buying questions. It tracks visibility and share of voice against competitors across ChatGPT, Gemini, Perplexity, Google AI surfaces, etc., with daily metrics. Peec AI
• otterly.ai — more lightweight. You can create a prompt set and track mentions, citations and SOV across multiple AI engines, with SOV broken down by prompt, engine and market. Otterly

For your exact use case

If you're trying to answer something like:

“For 500 long-tail queries around customer feedback software, product analytics for startups, NPS alternatives, etc., how often does my SaaS get recommended versus competitors?”

I'd lean toward Profound or Peec because you want prompt-level SOV, not just a broad AI visibility score.

One important distinction: AI tools generally don't have a conventional search-volume number for these long-tail queries. You're measuring the prompts you choose to monitor and the answers generated for them. So I'd build a representative prompt corpus, e.g. 200–1,000 buyer-intent questions, categorize them by use case/persona/stage, and calculate SOV by cluster as well as overall. Profound explicitly exposes prompt-level SOV and prompt volume; Peec lets you organize tracking around your own prompt library. Profound Help Center Peec AI

If you tell me roughly how many queries you want to track (100 / 1k / 10k+) and your monthly budget, I can narrow this down to the tools that can actually handle that scale.

Yes. This is emerging as a distinct GEO / AI-search intelligence category, and a few platforms now combine competitive AI Share of Voice with citation-level analysis.

The closest matches I found:

• tryprofound.com — probably the most established enterprise-style option. It tracks Share of Voice, visibility rank, citation share, sentiment, and average position, with competitor benchmarking across ChatGPT, Perplexity, Google AI Overviews, Gemini, Copilot, Grok, etc. Profound
• zumihq.com — particularly close to your wording. Its visibility model explicitly combines mention frequency + competitor SOV + position + the pages that AI cites, with "citation quality" influencing visibility. Zumi
• ranksage.com — interesting if by citation quality you mean evaluating whether the cited page actually supports the claim. It combines AI Share of Voice with Citation Integrity, checking whether cited URLs are live and whether they actually mention/support the brand. RankSage RankSage
• citationly.ai — combines competitor SOV with source-level citation analysis and explains which sources are driving competitor visibility. CITATIONLY CITATIONLY
• geoly.ai — has a competitor × AI-model matrix showing SOV, citation rate, visibility and rankings side-by-side. GEOly
• bluejar.ai — combines AI SOV/competitor benchmarking with a mapping of the citation sources that could be improved. BlueJar

The interesting gap

There is an important distinction between "citation tracking" and "citation quality scoring."

Most platforms can tell you:

"ChatGPT cited Competitor A 38% of the time and you 14%."

Far fewer appear to answer:

"Competitor A's citations are higher quality than yours because their sources are more authoritative, more directly relevant to the claim, more recent, more frequently cited, and better positioned in the answer."

RankSage's Citation Integrity gets closer to the latter, while Zumi explicitly incorporates citation quality into its visibility model. RankSage Zumi

So if you're thinking about this as a product concept, I would frame the missing layer as something like:

AI Share of Voice → Citation Share → Citation Quality → Competitive Citation Gap

For example:

That combination is considerably more actionable than a generic "AI visibility score."

If you're asking because you're evaluating whether to build/buy a platform, I can also map the current vendors across SOV, citation quality, source authority, competitor attribution, prompt-level data, and pricing and identify what appears genuinely underserved.

Yes. If your goal is specifically “take historical AI answers and show how our share of voice has shifted vs. competitors over time,” there are now several purpose-built platforms.

Strong options

• Profound — enterprise-oriented AI visibility/answer-engine intelligence. Good fit if you have a large prompt set, multiple brands/markets, and want historical competitive analysis.
• DemandSphere — tracks LLM visibility historically and explicitly supports position and share-of-voice vs. competitors across AI engines. DemandSphere
• RankZero — particularly focused on competitor head-to-heads, showing SOV by engine, prompt, and over time, plus the sources that competitors are getting cited from. RankZero
• Searchpanel — tracks competitors on the same scoring framework as your brand, including share of voice, head-to-head position, citations, and historical answer data. Searchpanel
• CrowdReply — interesting if you want the SOV movement tied directly to the prompts/models responsible for the change. It says it can show week-over-week changes and their underlying prompts and competitors. CrowdReply
• SearchDock — useful if you specifically want SOV calculated from stored historical captures, with separate trends for ChatGPT, Perplexity, Gemini, Claude, and Google AI Overviews. SearchDock
• Rankvia — has a clearly documented methodology for calculating AI SOV from observed answers and supports historical/platform-level competitive measurement. Rankvia

The key distinction I'd look for

If you already have a historical dataset of AI answers, don't just buy a tool that gives you a current “AI visibility score.” You want one that can preserve/reprocess the underlying observations:

date → prompt → AI engine → answer → brands mentioned → citations → position

Then you can calculate something like:

Competitor A SOV = AI answers mentioning Competitor A ÷ total competitor-brand appearances

and plot that monthly/weekly.

That's materially better than a dashboard that only gives you today's visibility score. Also check whether the platform lets you add a competitor later and backfill historical SOV; CrowdReply, for example, says it can backfill a newly added competitor from existing prompt data. CrowdReply

If you tell me (1) whether you already have the historical AI-answer data, (2) which engines—ChatGPT/Gemini/Perplexity/etc., and (3) roughly how many prompts, I can narrow this to the 2–3 tools that fit and compare their historical SOV capabilities.

Yes. There are now several tools specifically designed to measure AI/LLM share of voice and feed the data into an existing BI/reporting stack, rather than forcing you to use another standalone dashboard.

A few worth looking at:

• demandsphere.com — probably the most relevant if you have an established enterprise analytics stack. It offers REST APIs and can push AI/search data into BigQuery, Snowflake, Tableau, Power BI, Looker Studio, S3, etc. DemandSphere
• conductor.com — aimed at enterprise BI. Its API exposes AEO/AI-search intelligence for Looker, Tableau, Power BI and other BI environments. Conductor
• developer.meltwater.com — interesting if you already use Meltwater. Its API includes AI share of voice, mentions, sentiment, citations and trends, essentially extending its existing media/social analytics model into generative AI. Meltwater Developer Portal
• searchfit.ai — more API-first. It provides AI visibility/SOV, rankings and reports as structured JSON that can be piped into BI, data warehouses or internal reporting. SearchFIT
• docs.temso.ai — exposes daily brand mentions and SOV data through an API, with filtering by AI model, country, language and prompt type. Temso API Docs
• getintel.ai — particularly interesting if your reporting stack is Looker Studio. It exposes SOV and citation metrics as native fields and lets you blend them with GA4/Search Console data. GetIntel

What I'd look for architecturally

If you already have something like:

AI visibility platform → API/connector → Snowflake/BigQuery → Looker/Tableau/Power BI

that's preferable to exporting CSVs from an AI-search tool every month.

The important part is getting granular data, not just a single "AI visibility score." Ideally the API gives you:

• Share of voice by brand/competitor
• AI engine/model
• Prompt/query
• Mention/rank
• Citation URL/domain
• Date/time
• Geography/language
• Sentiment or answer framing
• Prompt category/funnel stage

That lets you calculate your own executive KPIs and join AI visibility against organic traffic, conversions, revenue, CRM data, campaign data, etc. Conductor and DemandSphere explicitly position their APIs for this kind of BI integration. Conductor DemandSphere

If you tell me what your existing stack is (e.g. Snowflake + Tableau, BigQuery + Looker, Power BI, HubSpot, GA4, etc.), I can narrow this to 2–3 tools that fit your architecture and show you what the integration would look like.

That lets you calculate your own executive KPIs and join AI visibility against organic traffic, conversions, revenue, CRM data, campaign data, etc. Conductor and DemandSphere explicitly position their APIs for this kind of BI integration.

Yes. There’s now a fairly mature category of tools for this, usually called AI Search Visibility, GEO (Generative Engine Optimization), or AI Share of Voice.

The important distinction is that the better platforms don't just tell you “your competitor was mentioned.” They can trace:

Buyer question → AI answer → competitor mentioned/recommended → source/citation → competitor content/page that influenced the answer.

For example, tryprofound.com tracks competitor visibility across AI platforms and lets you identify prompts where competitors are getting citations and compare the pages behind those citations. Profound

Other current products doing this include:

• citationly.ai — competitor questions won, citation sources, visibility gaps and share of voice across six AI engines. CITATIONLY CITATIONLY
• rankvia.ai — competitor share of voice, question-level gaps, and the sources influencing AI answers. Rankvia Rankvia
• searchpanel.ai — tracks competitor share of answers and the domains/pages being cited. Searchpanel
• ranqo.ai — particularly focused on identifying the exact prompts where a competitor appears and you don't, plus the sources powering their answers. Ranqo
• cyted.app — breaks down competitor recommendation position and the pages/domains supporting those recommendations. Cyted

What you're describing specifically

If your question is:

“Which competitor content is actually winning AI answers and taking share of voice from us?”

I'd look for a product that gives you all five of these layers:

1. AI Share of Voice — e.g. 24% vs. Competitor A's 41%.
2. Prompt-level competitive gaps — the exact buyer questions where A appears and you don't.
3. Answer position/recommendation — whether you're mentioned, recommended first, or merely cited.
4. Citation/source intelligence — which URLs, publishers, Reddit threads, reviews, comparison pages, etc. are causing the competitor to appear.
5. Content-level attribution — connecting those sources back to the competitor's content strategy.

That last piece is the really valuable one. A simple AI-visibility dashboard tells you who is winning; a competitive intelligence system should tell you what content/evidence is causing them to win.

There is also a useful emerging distinction between mention share and citation share. A competitor might be mentioned frequently because third-party sources recommend them, even though their own domain isn't what the AI cites. Some platforms therefore track both brand presence and source/citation share. Answer Engines Optimization Rankvia

If you're evaluating this as a marketing/SEO product to buy, I can also give you a side-by-side of Profound vs. Peec vs. Scrunch vs. Otterly vs. AthenaHQ vs. Citationly, specifically for “show me the competitor content taking AI share of voice”.

If you want to track which websites/domains ChatGPT and other AI engines cite for ecommerce queries, look at OtterlyAI and Ahrefs Brand Radar.

• OtterlyAI — tracks cited URLs/domains across ChatGPT, Gemini, Perplexity, Claude, Copilot and Google AI experiences. otterly.ai
• Ahrefs Brand Radar — shows cited domains/pages and lets you monitor custom prompts and competitors. ahrefs.com
• For an ecommerce blog, I’d particularly track non-brand prompts like “best X,” “X vs Y,” and “how to choose X,” then identify which domains repeatedly get cited.

OtterlyAI · Ahrefs AI Visibility Checker

If your goal is specifically to map which domains AI answer engines cite when shoppers ask product/category questions, I’d use an AI visibility / citation-monitoring platform, not ordinary rank tracking.

What I’d use

ahrefs.com is probably the most straightforward fit if you already use Ahrefs. It tracks AI answers across ChatGPT, Perplexity, Gemini, Google AI Overviews/AI Mode, Copilot and others, and lets you inspect cited domains and pages. It also supports custom prompts, so you can feed it the exact product questions your customers ask. Ahrefs Help Center Ahrefs

For example, you could track:

• “best running shoes for flat feet”
• “best [product category] under $100”
• “[product A] vs [product B]”
• “alternatives to [competitor]”
• “best [category] for [use case]”
• “is [product] worth it?”
• “[product category] reviews”
• “[product category] for beginners”

Then build a dataset like:

That distinction between mentions and citations matters: an AI can mention your product while citing someone else's site. Ahrefs explicitly separates those metrics and also records pages that were retrieved but not ultimately cited. Ahrefs Help Center

Another tool worth looking at

otterly.ai is more directly oriented around prompt-level AI search monitoring. It runs your prompt set across seven AI engines and records the answers, mentions, competitors and the URLs/domains cited in those answers. Otterly

So I'd think of the two roughly as:

• Ahrefs Brand Radar: better if you want to connect AI visibility with your existing SEO/search-demand data and discover which domains/pages are influencing AI answers. Ahrefs Ahrefs
• OtterlyAI: attractive if your primary job is monitoring a defined set of buyer prompts and their AI citations over time. Otterly

For an online store, I'd structure the analysis this way

Don't just measure “does AI cite my domain?”. Build a citation landscape:

1. Collect 100–500 high-intent product prompts across your major categories.
2. Run them across ChatGPT, Perplexity, Gemini, Google AI, etc.
3. Extract every cited URL.
4. Normalize URLs into domains.
5. Group domains into categories:

• publishers/reviews
• Reddit/forums
• retailers/marketplaces
• manufacturers/brands
• comparison sites
• affiliates
• your own domain
• Calculate citation frequency by domain × engine × product category.
• Separately track which specific pages are repeatedly cited.
• Compare that with your own pages and competitors' pages.

The really interesting output becomes something like:

For “best X” queries, 37% of citations come from review publishers, 22% from Reddit, 15% from retailers, 11% from manufacturers…

and then:

Domain X is cited 4.2× as often as our store for category Y, primarily because of these three URLs.

That tells you much more than an “AI visibility score.”

One caveat: AI-answer monitoring is inherently sampling-based. You can't observe every private ChatGPT conversation or every AI answer, so treat the resulting domain map as a representative measurement of your tracked prompts, not an absolute census. Ahrefs makes this limitation explicit. Ahrefs

If you tell me what platform your store is on (Shopify, WooCommerce, custom, etc.) and roughly how many product categories you have, I can lay out a concrete AI citation-tracking stack + spreadsheet/database schema + prompt set for doing this at scale.

Yes. What you’re looking for is generally called an AI visibility / AI citation tracking platform. These tools run a controlled prompt set across multiple assistants, collect the answers and cited URLs, then aggregate the results into metrics such as citation rate, mention rate, competitor share of voice, and citation share.

For a SaaS marketing team, I’d shortlist:

• tryprofound.com — enterprise-oriented AI search intelligence; useful if you need deep citation/source analysis and reporting across multiple AI engines. Recent industry research is using Profound data across ChatGPT, Perplexity, Gemini, Copilot and Google AI surfaces. Tinuiti
• peec.ai — focused specifically on AI visibility/GEO tracking, including competitor comparisons.
• otterly.ai — more accessible for teams/agencies wanting recurring AI visibility and citation monitoring.
• citationly.ai — explicitly tracks citations, mentions, competitors and share of voice across ChatGPT, Gemini, Claude, Perplexity, Copilot and Grok. CITATIONLY
• cloro.dev — particularly interesting if your SaaS team wants API-accessible raw citation data rather than just a marketing dashboard. It says its API returns parsed citations, source URLs, positions and labels across seven engines, which you can aggregate yourself into SOV. cloro

If you're building a reporting stack

I'd distinguish two use cases:

"Give me a dashboard" → Profound / Peec / Otterly

"Give me the underlying citation dataset so we can pipe it into our own BI/data warehouse" → Cloro or another API-first provider.

For your specific phrase "aggregate citation data from different AI assistants", the API approach is especially attractive. Your canonical dataset could look like:

date × prompt × AI engine × brand × cited URL × citation position × mention × recommendation

Then your marketing dashboard can calculate:

• AI Share of Voice = your brand's appearances ÷ all tracked brand appearances
• Citation Share = answers citing your domain ÷ all answers
• Mention Rate = answers mentioning your brand ÷ total answers
• Citation Position = average position when your domain is cited
• Competitor SOV by prompt category
• Engine-specific SOV — ChatGPT vs. Perplexity vs. Gemini, etc.

That's important because simply combining all assistants into one number can obscure substantial differences between engines. Current AI-visibility methodologies generally recommend retaining the per-engine breakdown. organikpi.com geosoftwarerankings.com

If you tell me your SaaS category, approximate number of prompts, and whether you want an API or just a dashboard, I can narrow this to 2–3 options and compare pricing, API access, engine coverage, and reporting capabilities.

Then your marketing dashboard can calculate:

• AI Share of Voice = your brand's appearances ÷ all tracked brand appearances
• Citation Share = answers citing your domain ÷ all answers
• Mention Rate = answers mentioning your brand ÷ total answers
• Citation Position = average position when your domain is cited
• Competitor SOV by prompt category
• Engine-specific SOV — ChatGPT vs. Perplexity vs. Gemini, etc.

That's important because simply combining all assistants into one number can obscure substantial differences between engines. Current AI-visibility methodologies generally recommend retaining the per-engine breakdown.

If you mean “find the questions/topics where AI answers currently lack our research as a citation, then turn those gaps into content opportunities,” the closest fits are:

• tryprofound.com — probably the strongest match for an established content/AEO team. It tracks which sources ChatGPT, Perplexity, Google AI Overviews and others cite, shows where competitors are getting citations instead of you, and lets you analyze gaps by prompt/topic. Profound Profound Help Center
• scrunch.com — particularly relevant if the workflow is content research → identify citation gaps → optimize existing content. Its citation view records the actual URLs cited for tracked prompts and can be filtered by topic to identify gaps. Scrunch
• citations.io — more explicitly built around citation intelligence. It identifies sources AI cites, finds gaps, and turns the evidence into prioritized content/PR/citation work. Citations
• citlyze.com — useful if you want the workflow framed around “competitor is cited for this prompt, we're not” and then generate an actionable content recommendation. Citlyze
• learn.microsoft.com — worth checking if you already use Clarity; its citation dashboard specifically surfaces citation activity and coverage gaps. Microsoft Learn

For your exact use case

I'd shortlist Profound vs. Scrunch vs. Citations.io.

The key distinction is that you don't merely want an AI visibility score. You want a workflow like:

Question → AI answer → citations used → missing/competing sources → “our research could substantiate this claim” → content brief/update

Profound and Scrunch are particularly strong on the citation-gap discovery side, while Citations.io emphasizes turning those findings into implementation work. Profound Scrunch Citations

If you tell me whether your research is original data/reports, academic research, surveys, or editorial content, I can narrow this to the 2–3 platforms that best fit that workflow.

If you want to see when GPT-style crawlers hit your site and graph crawl frequency over time, you generally need a server-side bot analytics/logging tool (Google Analytics won’t capture most AI crawlers because they don’t run browser JavaScript). DataEase AI Ahrefs Help Center

Good options:

The data you usually want to capture is:

• User-Agent: e.g. GPTBot, OAI-SearchBot, ChatGPT-User, ClaudeBot, PerplexityBot
• Timestamp
• URL requested
• HTTP status code
• Request frequency over time
• Pages most/least crawled

A simple DIY stack many developers use:

1. Enable access logs (Nginx/Apache/Cloudflare).
2. Filter requests where the user-agent matches AI crawlers.
3. Send events to a dashboard tool such as Grafana, Looker Studio, or a database.
4. Chart:

• crawls/day by bot
• crawls/week by page
• recrawl intervals

One caveat: a crawler hit means a bot fetched your page; it does not necessarily mean your content appeared in ChatGPT answers or other AI outputs. Crawl activity and AI citations/mentions are separate measurements. DataEase AI

If you tell me your stack (WordPress, Shopify, Cloudflare, AWS, nginx, etc.), I can suggest the simplest setup.

Yes. What you’re looking for exists, but the market is split into a few categories:

1. AI crawler access monitoring — tells you when AI bots (GPTBot, ClaudeBot, PerplexityBot, Google-Extended, etc.) hit specific URLs.
2. Server/CDN log analytics — gives the most accurate “who accessed /product/xyz at 2:14 AM?” visibility.
3. AI visibility/GEO platforms — focus more on whether AI engines mention your products, not raw crawling.

For an ecommerce site with high-value pages (top products, pricing pages, category pages, proprietary content), these are the closest fits:

For your specific use case — “alert me when an AI crawler accesses these 50 high-value product URLs” — I would look for:

• URL-level tracking (not just domain-level)
• Bot identity verification (avoid fake GPTBot user agents)
• Slack/email/webhook alerts
• Crawl frequency trends
• Exportable logs
• Integration with Shopify/BigCommerce/custom server logs if applicable

A common setup for ecommerce teams is:

• Cloudflare/Akamai/Fastly logs → AI crawler dashboard → alerts
• Maintain a “VIP URL list”:

• /products/high-margin-product-a
• /collections/top-category
• /pricing
• /compare/...
• Trigger alerts when:

• GPTBot/ClaudeBot/PerplexityBot hits a VIP page
• crawl frequency spikes unexpectedly
• a crawler starts receiving 403/429 responses
• robots.txt or firewall rules change

If you tell me your stack (Shopify, Magento, WooCommerce, custom, Cloudflare or not) and whether you want alerts on every hit or only unusual activity, I can narrow this down.

Yes. There are now a few options specifically aimed at AI crawler / bot traffic, and some can feed the data into an existing BI + warehouse architecture rather than forcing you to use their dashboard.

The strongest fit: Cloudflare

If your sites are already behind Cloudflare, I'd look closely at AI Crawl Control.

Cloudflare exposes AI-crawler analytics through a GraphQL Analytics API, including crawler, operator, path, status-code, referrer, and data-transfer dimensions. That means you can extract the data into your existing warehouse and build the reporting in Tableau, Power BI, Looker, Sigma, etc. Cloudflare Docs Cloudflare Docs

For example, your warehouse could have:

ai_crawler_hits
├── timestamp
├── domain
├── path
├── crawler
├── operator
├── crawler_category
├── status_code
├── bytes_transferred
├── referrer
└── request_count

You could then join that against your existing content, web analytics, and revenue tables:

AI crawler activity
        ↓
   DATA WAREHOUSE
        ↓
 ┌──────┴─────────┐
 │                │
Content data   Web analytics
 │                │
 └──────┬─────────┘
        ↓
   Existing BI

Cloudflare specifically says its GraphQL API can be used to build custom reports and integrate the data with monitoring/analysis systems. Cloudflare Docs

Other options

• Ahrefs Bot Analytics — server-side bot tracking through Cloudflare, with AI-crawler filtering. This is more oriented toward SEO/marketing analytics than acting as a warehouse-native ingestion layer. Ahrefs
• Botsanalyser — interesting if you specifically want an ingestion/API layer. It claims integrations with BigQuery, GA4, Looker, HubSpot and Segment, in addition to its own dashboard. Bots Analyser
• DataEase AI — focused specifically on AI-crawler analytics and capturing crawler activity at the edge rather than through JavaScript analytics. DataEase AI
• Build directly from server/CDN logs — potentially the most flexible option if you already have access to HTTP logs. You can classify GPTBot, ClaudeBot, Google-Extended, PerplexityBot, Bytespider, etc., and land the events directly in your warehouse.

One important distinction

I'd separate AI crawler hits from AI-referred human traffic.

For example:

Cloudflare's current tooling covers a particularly useful subset of this: crawler activity, pages, operators, status codes, bandwidth, and AI-driven referrals. Cloudflare Docs

If your requirement is specifically "get AI crawler events into our existing warehouse and visualize them in our existing BI," I'd investigate Cloudflare's GraphQL API first, especially if you're already using Cloudflare. If you tell me your warehouse (Snowflake/BigQuery/Redshift/etc.) and BI tool (Tableau/Power BI/Looker/etc.), I can map out the exact architecture and identify which products have native connectors versus requiring a small ETL job.

If your main goal is “How often does my brand appear in major AI assistants, and exactly what URLs are cited for each mention?”, I’d look first at otterly.ai.

It specifically tracks:

• Brand mentions/coverage across ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini, Copilot, and Claude. Otterly Otterly AI
• Citation details, including the URLs/domains AI systems use as sources. Otterly AI Otterly AI
• Prompt-level results, so you can see the exact question asked, the resulting AI response, whether your brand appeared, and which sources were cited.
• Competitor visibility and share of voice, useful for seeing where competitors are appearing when you aren't. Otterly
• Exports/API access if you want to pipe the data into your own reporting system. Otterly

Other tools worth comparing

tryprofound.com is probably the other one I'd evaluate. Its Answer Engine Insights product tracks brand visibility and citations and lets you drill into which sites/pages are influencing AI answers. Profound Help Center Profound

ahrefs.com is useful for a quick/free snapshot of how a brand appears across ChatGPT, Gemini, Perplexity, Copilot, and Google AI Overviews, but it's less suited to being your detailed ongoing citation-monitoring system. Ahrefs

My practical shortlist:

1. OtterlyAI — strongest fit if mention frequency + citation-by-citation tracking is the core requirement.
2. Profound — strong if you also want deeper analysis of the sources influencing AI answers.
3. Ahrefs — useful as a lightweight/free visibility check.

If you tell me your brand, industry, and roughly how many prompts you want to track, I can also compare OtterlyAI vs Profound vs Ahrefs on price, AI engines, citation granularity, API access, and reporting.

For a small marketing team, I’d shortlist:

• Otterly.AI — easiest low-cost starting point; multi-LLM monitoring and share-of-voice tracking, with plans reported from ~$29/month. Toolsolved Geohats
• Peec AI — better if you need richer competitor benchmarking, multilingual tracking, and more marketing-oriented analytics; reported starting around €85–95/month. Geohats The Best Marketing Tool
• Profound — substantially more enterprise-oriented; deeper analytics and broader coverage, but likely overkill for a small team. Geohats

My starting point: Otterly.AI if your primary goal is simply “What percentage of relevant AI answers mention us vs. competitors?” Peec AI if competitive analysis is equally important.

If your goal is specifically “Are AI assistants mentioning/citing us, and does that translate into incremental traffic?”, I’d separate the problem into two layers:

1. AI-answer visibility: prompts, mentions, citations, share of voice, competitors, cited URLs.
2. Actual business impact: AI-referred sessions, conversions, pipeline/revenue, and uplift after visibility changes.

The market is still young, and the tools generally measure visibility better than they measure true incremental causality. Marqeable Austin Heaton

Shortlist

For example, Otterly currently tracks mentions, citations, sentiment and share of voice across seven AI engines, including ChatGPT, Perplexity, Gemini, Claude and Google's AI experiences. Otterly Profound is positioned more toward enterprise-grade AI-search analytics and attribution. Lectern PickMySoft

What I'd actually measure

Don't make “AI visibility score” your primary KPI. I'd build a funnel like:

Prompts → AI mention → AI citation → cited URL → AI referral → engaged session → conversion → revenue

That distinction matters because a brand can be mentioned without receiving a link or traffic. Citation tracking is therefore much more useful for your stated goal. Austin Heaton

Then use GA4 (or your product analytics) alongside the AI-visibility platform to measure:

• AI-referred sessions by assistant
• landing pages receiving AI traffic
• engagement/conversion rate of AI visitors
• assisted conversions
• pipeline/revenue from AI referrals
• AI visibility/citation share before vs. after content changes
• competitors gaining/losing citation share
• which specific pages are being cited

My practical recommendation

If you're a larger marketing organization and traffic/pipeline attribution is central, I'd investigate Profound first.

If you're primarily trying to monitor AI answers and build an ongoing GEO/AEO reporting workflow, compare Peec AI and OtterlyAI.

If you already have Semrush or Ahrefs, I'd test their AI features before adding another standalone platform—the benefit is having your conventional SEO and AI-search data in one reporting environment.

One important caveat: no platform can perfectly measure “traffic uplift caused by being featured.” AI responses are dynamic and personalized, and different trackers sample different prompts. The strongest setup is therefore AI visibility tracking + first-party analytics + controlled before/after measurement, rather than relying on a single proprietary visibility score. Austin Heaton Marqeable

If you tell me your company size, monthly website traffic, B2B/B2C, and whether you're primarily interested in ChatGPT vs. all AI search, I can narrow this to 2–3 tools and compare their current pricing/features.

Yes. The category you’re looking for is AI visibility / GEO (Generative Engine Optimization) software. These tools query AI assistants with buyer questions and show where competitors are mentioned or cited while your brand is absent, often down to the specific prompt and source being cited. Semrush Promptwatch

A few relevant options:

• Profound — enterprise-focused monitoring of AI search visibility, competitor presence, and citations.
• Peec AI — tracks brand visibility and competitors across AI search engines.
• OtterlyAI — more lightweight monitoring of mentions, citations, and competitor visibility.
• Semrush AI Visibility — useful if you already use Semrush and want AI visibility alongside conventional SEO. Semrush
• Frase — particularly focused on turning visibility gaps into content changes; it monitors prompts and identifies lost mentions. Frase
• jujuGEO — explicitly identifies questions where an AI engine names a competitor instead of you, then helps draft content intended to close that gap. jujuGEO

The specific capability to look for

If your goal is “Show me every important question where ChatGPT/Perplexity/Gemini answers with Competitor X's content instead of ours”, don't settle for a simple brand-monitoring tool. Look for these features:

1. Prompt discovery — automatically finds questions your customers might ask.
2. Competitor gap detection — identifies prompts where competitors appear and you don't.
3. Citation/source analysis — shows which pages AI is using to support the competitor's answer.
4. Content gap analysis — explains what information is missing from your own content.
5. Multi-engine tracking — ideally ChatGPT, Gemini, Perplexity, Google AI, and Claude.
6. Historical monitoring — lets you see whether the gap is persistent or just one model response.

For example, a useful report would look like:

Prompt: “Best CRM for a 50-person SaaS company”
ChatGPT: Competitor A, Competitor B
You: Not mentioned
Sources cited: G2, Competitor A comparison page, industry publication
Gap: No authoritative page on your site addressing implementation cost for 50-person SaaS teams.

That prompt → competitor → cited source → missing content chain is the key functionality.

If you tell me your company/category and whether you're targeting B2B, B2C, or ecommerce, I can identify the tools that specifically support this workflow and compare their current pricing/features.

If your goal is creating content outlines that increase the chance of being quoted/cited by conversational AI systems (ChatGPT, Gemini, Perplexity, Claude, AI Overviews, etc.), you’re looking for tools in the GEO (Generative Engine Optimization) / AEO (Answer Engine Optimization) category rather than traditional SEO brief tools. These tools usually focus on question targeting, entity coverage, citation patterns, and AI visibility tracking. GeoCopy SitePoint

Here are the strongest options by use case:

Best for AI-quotable content briefs

frase.io

Best for: Solo writers, content teams, SEO strategists

Why it fits:

• Builds research-driven outlines from SERP and competitor analysis
• Finds missing topics/entities competitors cover
• Encourages question-based sections and concise answers (useful for AI retrieval)
• Good bridge between classic SEO and GEO-style formatting

Use it when you want:

“Create an authoritative article outline that covers everything an AI answer engine might need.”

surferseo.com

Best for: Teams already doing SEO at scale

Useful features:

• Content editor and topical coverage analysis
• NLP/entity suggestions
• Competitive content gap analysis

It is more SEO-first than GEO-first, but strong outlines often translate into better AI retrieval because they improve topical completeness. MarqOps

Best for tracking whether AI models actually quote you

tryprofound.com

Best for: Enterprise teams

Strengths:

• Tracks AI visibility and citations
• Helps identify which sources AI systems reference
• Useful for reverse-engineering what kinds of pages get cited CrowdReply MarqOps

This is less of an outline generator and more of a “why are we or competitors being mentioned?” tool.

peec.ai

Best for: Monitoring AI mentions and citations

Useful if your workflow is:

1. Create content
2. Publish
3. Measure whether AI engines start referencing it

GitHub

Best all-in-one GEO workflow tools

writesonic.com

Best for: Teams wanting AI content creation + optimization

Combines:

• Content generation
• SEO optimization
• AI visibility features

Useful if you need a single platform rather than assembling several tools. CrowdReply

semrush.com

Best for: Existing marketing teams

Good fit if you already use it for:

• Keyword research
• Competitor analysis
• Content planning

Its AI visibility features extend a traditional SEO workflow into AI search monitoring. GitHub

My recommended stack for creating “AI-citable” outlines

For most content teams:

1. Research + outline creation

• Frase or Surfer
• AI citation intelligence

• Profound or Peec AI
• Final outline rules

• Start each section with a direct answer
• Use question-style H2s
• Include definitions, comparisons, tables, statistics, and named sources
• Add original data/examples where possible
• Make key claims easy to extract as standalone passages

A good AI-oriented outline usually looks less like a traditional blog outline:

Introduction → history → overview → conclusion

and more like:

What is X?
How does X work?
What are the main types of X?
X vs Y comparison table
Expert examples/data
Common mistakes
FAQ

That structure gives conversational systems more self-contained passages to quote.

If you tell me your niche (SaaS, ecommerce, finance, health, publishing, etc.) and whether you care more about ChatGPT citations, Google AI Overviews, or Perplexity, I can suggest a tighter stack.

If your goal is specifically “show me which page/content formats AI assistants actually cite”, I’d look at HubSpot’s AEO tooling first. It tracks visibility across answer engines and pairs that with research on citation patterns. Its 2026 research analyzed AI citations across ChatGPT, Gemini, Google AI Overviews, and Perplexity, including format-level differences such as listicles, articles, product pages, comparisons, and how-tos. HubSpot Blog HubSpot Blog

blog.hubspot.com

A useful distinction, though: there isn't one universally dominant format. For example, a 2026 study of 25,337 citations found articles at 23.7%, listicles at 19.6%, and product pages at 16.3%, while noting substantial variation by industry. DeltaV Digital

If you want a more specialized citation-analysis tool, I'd also investigate Peec AI. It is used in AI-search research specifically to analyze what gets cited, and its data underpins Wix Studio's large-scale AI citation research. HubSpot Blog

What I'd want the tool to report:

• Citation share by format: listicle, how-to, comparison, product, category, documentation, etc.
• Breakdown by ChatGPT vs. Gemini vs. Perplexity vs. Google AI
• Citation rate by search intent/buyer stage
• Competitor pages that get cited for the same prompts
• Which formats are underrepresented on your site relative to cited competitors

If you tell me whether you're looking for a SaaS tool to buy or a free research tool/report, I can narrow this to 2–3 concrete options and compare pricing/features.

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.

For a small B2B content team, I’d avoid buying a huge “AI SEO” platform first. Your goal is slightly different from traditional SEO: you want your content to be retrievable, trustworthy, and citable by AI assistants. Tools in this category are often called GEO (Generative Engine Optimization) or AI visibility tools. They typically track whether AI systems mention brands, cite URLs, and which competitors appear instead. Elmo Semrush

My shortlist:

For a small B2B content team, my practical setup would be:

1. Use a content optimizer (Frase, Surfer, Clearscope, etc.) while drafting:

• cover the questions buyers actually ask
• add clear definitions and comparisons
• include original data, examples, and expert quotes
• Use an AI visibility tracker (Otterly.ai, Peec AI, Semrush, etc.) monthly:

• create 20–50 buyer prompts:

• “best [category] tools for mid-market companies”
• “[your competitor] vs [your product]”
• “how to solve [problem your product solves]”
• track whether your pages appear as cited sources
• Optimize for citation, not just rankings:

• Put concise answers near the top of articles
• Add statistics with sources
• Publish original research, benchmarks, templates, and frameworks
• Make authorship and expertise obvious
• Keep pages updated

If you only want one tool to start, I’d pick:

• Already paying for SEO software → Semrush
• Need a focused AI citation monitor → Otterly.ai or Peec AI
• Need help rewriting dozens of blog posts → Frase

One caveat: no tool can guarantee that ChatGPT, Gemini, Perplexity, or other assistants will cite you. These systems choose sources based on many factors, and the measurement landscape is still developing. arXiv The tools are most useful for finding gaps and measuring progress, not for “gaming” citations.

If you tell me your stack (WordPress/Webflow/HubSpot, monthly article volume, and whether you sell SaaS/services/hardware), I can narrow this to a specific workflow and budget.

For an enterprise marketing team with strict compliance, I’d look closely at Profound and OtterlyAI. They address the specific problem of measuring how AI assistants/search engines use and cite your content, rather than just traditional SEO.

• tryprofound.com — particularly relevant if compliance/security is a major buying criterion. Its Answer Engine Insights tracks brand visibility and citations across AI answer engines, and its site states that it is SOC 2 Type II compliant. It also connects citation data to content optimization workflows. Profound Profound
• otterly.ai — strong for broad AI-search monitoring. It tracks mentions and URL citations across ChatGPT, Perplexity, Gemini, Google AI experiences, Copilot, and Claude, with prompt monitoring, citation analysis, content audits, and recommendations. Otterly Otterly

For your use case

If strict compliance and enterprise security controls are the primary requirement, I'd put Profound through your security/procurement review first, specifically verifying its current DPA, data-retention terms, SSO/RBAC, audit logging, data residency, subprocessors, and whether customer prompts/content are used for model training.

If the priority is tracking exactly where your content gets cited and finding content gaps across many AI platforms, OtterlyAI is a compelling alternative. It also offers API/MCP access for integrating visibility data into existing workflows. Otterly Otterly AI

One important distinction: these tools primarily measure AI-search visibility and citations. They don't necessarily tell you every instance in which an AI assistant has consumed or trained on your content; those are technically different things.

If you tell me your compliance requirements (e.g. SOC 2, ISO 27001, GDPR, data residency, SSO/SAML, no training on customer data), I can compare Profound vs. OtterlyAI against those criteria.

If your goal is specifically “make my guides easier for ChatGPT, Perplexity, Gemini, and Google AI Overviews to discover, quote, and cite,” you’re looking for GEO (Generative Engine Optimization) / AI-search optimization rather than ordinary SEO. Semrush arXiv

A few platforms worth looking at:

• semrush.com — Best if you already use it for SEO and want to add AI-search visibility/optimization to the same workflow. It covers AI search optimization and visibility tracking. Semrush
• otterly.ai — More specifically focused on monitoring how brands/content appear in AI answers and optimizing for citation visibility. Otterly
• citare.ai — Particularly interesting for a niche publisher: it focuses on measuring citations across multiple AI platforms and turning those findings into a content roadmap. Citare Citare
• geodaddy.dev — A more technical, audit-oriented option that checks things such as crawlability, content structure, structured data, and AI-bot access. GeoDaddy

What I'd prioritize for a niche blog

I wouldn't just buy a tool that promises an arbitrary “GEO score.” Current research suggests AI citation is a multi-stage process—discovery, retrieval, citation, and how much of your content actually gets incorporated—and there isn't yet a universally reliable optimization formula. arXiv arXiv

For your guides, I'd want a platform that helps you:

1. Rewrite sections into self-contained, quotable answers.
2. Put the answer near the beginning of each section, rather than burying it.
3. Add concrete facts, definitions, comparisons, examples, and procedures that an assistant can extract cleanly.
4. Improve headings, HTML structure, internal linking, and structured data.
5. Monitor actual citations across ChatGPT, Perplexity, Gemini/Google AI search, etc., rather than assuming optimization worked.
6. Identify which guides/questions you're not being cited for and feed that back into your editorial process.

That structure is consistent with current guidance from search platforms and with research finding that highly cited/influential pages tend to be structured and rich in extractable evidence. Squarespace Help arXiv

For a niche publisher, I'd start with Citare or OtterlyAI for measurement, then use the resulting gaps to improve the actual guides. The important distinction is that formatting a page for AI and demonstrating that AI assistants actually cite it are two different jobs.

If you tell me your niche + CMS (WordPress, Ghost, Squarespace, etc.) + roughly how many guides you have, I can narrow this to 2–3 platforms and explain what each would actually do to your articles.

If by “exactly which snippets” you mean which of your pages AI assistants cite and what passages appear to be informing the answer, the closest tools I’d look at are Profound and Scrunch.

1. Profound — closest to your full requirement

tryprofound.com

Profound tracks prompts across ChatGPT, Perplexity, Google AI Overviews and other answer engines, captures the generated responses, and identifies the URLs being cited. It also has content-optimization workflows that analyze your pages against live citation data and suggest changes. Profound Profound

The useful distinction is:

• “Is my site being used?” → yes, citation tracking.
• “Which prompts use it?” → yes.
• “Which URL is contributing?” → yes.
• “What should I change?” → yes, through its content optimization/Agents features. Profound
• “Show me the exact sentence/paragraph from my page that the model copied or relied on.” → this is trickier. AI systems generally don't expose a definitive attribution map from each generated sentence back to an exact source passage.

2. Scrunch — particularly good for citation/source analysis

scrunch.com

Scrunch lets you inspect the actual AI responses for monitored prompts and see the sources cited in each response. Its citation interface can drill down from domain → URL → individual prompt, and it provides information about which pages contributed to answers. Scrunch Help Center Scrunch

It also crawls your site and provides page-level metrics such as AI-agent traffic, citations and an audit score, which is useful for determining which content to improve first. Scrunch Help Center

The important caveat

There isn't currently a tool that can reliably say:

“This sentence in ChatGPT's answer came specifically from paragraphs 4 and 7 of your article, and here's a confidence score proving that.”

That's because the underlying models don't generally expose their retrieval/context-selection process. A citation tells you the source, not necessarily the precise text that caused each generated claim.

What you can build is a very useful approximation:

AI answer → cited URL → relevant passages on your page → semantic/claim overlap → recommended rewrite

For example:

That gets much closer to the “tell me exactly what content AI is using and how I should improve it” workflow you're describing.

If you're building an AI-content/SEO product rather than just looking for a SaaS tool, I can also map out the architecture for doing this yourself—crawler + prompt runner + citation extraction + passage matching + claim attribution + content recommendations.

If your goal is “make our educational guides more likely to be accurately cited by AI, then monitor whether that actually happens,” I’d look closely at ** and .

How they differ

• Scrunch — particularly strong for citation monitoring and ongoing visibility tracking. It can

If you mean a tool specifically built to shape FAQ/Q&A content for AI assistants and answer engines, a few stand out:

• Jasper’s FAQ Generator Agent — specifically generates answer-first Q&A based on real query patterns, formats it for FAQ schema, and is designed around AEO/GEO and AI citation. Jasper Jasper
• Optimizely FAQ Creation Agent — extracts or generates question/answer pairs from existing pages and structures them for direct citation and FAQ schema. Optimizely
• Profound — broader AEO platform, but its content-optimization workflow includes an FAQ generator shaped around real user prompts and what answer engines reward. Profound
• Purple Leaf Q&A Optimizer — more narrowly focused on analyzing existing Q&As, finding missing high-intent questions, rewriting answers, and producing FAQ schema. Viziquo

For product FAQ pages specifically, Jasper is the closest match to “shape Q&A for assistant consumption,” while Optimizely is particularly useful if you already have product pages and want to turn their existing content into AI-friendly Q&A. These tools can improve structure and extractability, but none can guarantee that an assistant will cite or surface a page. Jasper Optimizely

If you tell me whether you're optimizing 10 product pages or thousands, I can narrow this down to the most suitable workflow.

If your priority is multilingual content + tracking which URLs AI assistants actually cite, I’d shortlist these platforms:

My practical take

OtterlyAI is probably the first one I'd evaluate for your use case. It explicitly supports 65+ countries and languages, lets you segment AI visibility by market, and tracks the actual URLs cited in AI answers. It covers ChatGPT, Google AI Overviews/AI Mode, Gemini, Perplexity, Copilot and Claude. Otterly Otterly

That combination is important because you don't just want to know "are we mentioned?" You want something closer to:

Language → market → prompt → AI engine → brand mention → cited URL → competitor cited instead → content/action to improve

Otterly also provides content audits and GEO recommendations rather than stopping at measurement. Otterly

Profound is worth testing alongside it if you're an enterprise organization and source-level citation intelligence, larger-scale monitoring, and enterprise controls matter more than simplicity. Independent 2026 comparisons put Profound toward the enterprise end of the market. Marqeable SEO Counselors

One thing I'd specifically test in demos: don't accept “supports 100 languages” as sufficient. Ask vendors to demonstrate the same 20–50 prompts in, say, English, German, French, Japanese and Spanish and show you the actual cited URLs by language/market. Multilingual AI visibility can differ substantially even when the underlying topic is identical.

If you tell me roughly how many languages/markets, domains, and prompts you need to track, I can narrow this to 2–3 platforms and compare their pricing, API/reporting, citation accuracy, and multilingual capabilities.

A few tools now do this. OpenLens and Ahrefs Brand Radar are notable options: they aggregate brand visibility across multiple AI answer engines and show mentions, citations, competitors, and trends in one dashboard. OpenLens Ahrefs

• OpenLens — ChatGPT, Claude, Gemini, Google AI, Perplexity, Grok, DeepSeek. OpenLens
• Ahrefs Brand Radar — ChatGPT, Gemini, Perplexity, Copilot, Google AI Overviews/Mode, plus broader visibility data. Ahrefs
• SearchInsight AI — adds Grok and Copilot to its cross-platform monitoring. searchinsight.ai

If you tell me your budget and whether you're an SEO team, agency, or in-house marketing team, I can compare the leading options.

Yes. The category you’re looking for is usually called AI search visibility, LLM visibility, GEO (Generative Engine Optimization), or AEO monitoring.

These tools repeatedly run a set of customer-style queries against multiple AI systems and report things such as:

• Whether your brand is mentioned
• Where your brand appears in the answer (e.g. #1, #3, absent)
• Share of voice versus competitors
• Which queries you win/lose
• Which competitors appear instead
• Which websites/sources the AI cites
• Changes in visibility over time

Tools worth looking at

Current coverage varies substantially between vendors, so I wouldn't choose based solely on the number of models they advertise. For example, Linksii explicitly tracks ChatGPT, Claude, Gemini and Perplexity, while OpenLens says it monitors seven platforms. Linksii OpenLens

Ahrefs is interesting if you already use it for SEO: its current AI Visibility Index is based on hundreds of millions of modeled prompts and reports visibility across multiple AI/search surfaces. Ahrefs

The feature I'd prioritize

If your actual question is "What queries does our brand rank for in AI?", make sure the product supports prompt-level tracking, not just a generic "AI visibility score."

For example, you want a report resembling:

That lets your SEO/content team identify specific prompts where you are absent or being displaced, rather than having a single opaque score.

One important caveat: AI "rankings" aren't equivalent to Google rankings. Responses can vary with model, location, personalization, web-search state, and time. Good monitoring platforms therefore run the same prompts repeatedly and report trends rather than treating one response as a definitive rank. Depra AI Prism

If you tell me your approximate company size (startup / mid-market / enterprise), monthly budget, and which AI systems matter most, I can narrow this to 3–5 platforms and compare pricing, prompt limits, engine coverage, API/export capabilities, and competitor tracking.

If you mean “alert me when ChatGPT, Claude, Gemini, Perplexity, Copilot, Google AI Overviews, etc. cite my website/content as a source”, you’re looking for an AI visibility / AI citation monitoring (AEO/GEO) tool. These are the main categories and options: TechnologyAdvice Unite.AI

A few things to check before choosing:

• Citation alerts vs mentions: Some tools tell you “your brand appeared”; fewer tell you “this exact URL from your site was cited as the source.” Those are different signals. Ahrefs CitePrism
• Coverage: Confirm the assistants you care about (ChatGPT, Claude, Gemini, Perplexity, Google AI Overviews, Copilot) are included. MentionScout CitePrism
• Prompt monitoring: The best systems usually let you define the questions your customers ask and alert you when your visibility changes for those queries. MentionScout

If your goal is specifically “email/Slack alert me whenever an AI assistant cites one of our URLs”, I’d shortlist MentionScout, Profound, Scrunch, and CitePrism based on citation-focused features. TechnologyAdvice MentionScout CitePrism

If you tell me your stack (enterprise SEO team, content marketing, SaaS, publisher, ecommerce, etc.) and which assistants matter most, I can narrow it down.

Yes. What you’re looking for is generally called an AI visibility tracker, AI search visibility platform, or GEO/AEO tracker.

These tools repeatedly run a set of relevant customer queries through multiple AI answer engines and record whether your site is mentioned, recommended, or cited—and often which exact page was cited.

A few options worth looking at:

• ahrefs.com — tracks mentions and citations across ChatGPT, Gemini, Perplexity, Copilot, and Google AI Overviews. Ahrefs specifically distinguishes a mention from a citation. Ahrefs
• citations.io — tracks ChatGPT, Gemini, Perplexity, and Claude, including the exact domains/pages cited and how often you're cited. It starts at $39/month. Citations
• ai-visibility.io — explicitly has a plan aimed at agencies and freelancers, with 100 prompts/site, ChatGPT/Gemini/Perplexity tracking, competitors, historical trends, CSV export, and white-label capabilities. AI Visibility
• deepsmith.ai — tracks mention rate, citation rate, and share of voice across ChatGPT, Gemini, Perplexity, Claude, and Google AI Mode, with the underlying answers available for inspection. DeepSmith
• koalr.ai — focuses on whether you're mentioned, cited, or recommended, with coverage across multiple AI answer engines. Koalr

For a freelancer, I'd look for these specific capabilities

The important distinction is that you probably don't just want "does AI mention my business?" You want something closer to:

"Of the 200 questions potential clients ask AI about my service, how many times did AI surface my website, and exactly which pages did it use?"

So I'd prioritize:

1. Prompt tracking — you define questions such as “best freelance UX designer for SaaS startups.”
2. Multiple engines — ChatGPT, Gemini, Perplexity, Claude, Google AI Overviews/AI Mode, etc.
3. Citation-level tracking — tells you which URL on your site was surfaced.
4. Historical tracking — e.g. citation rate went from 8% → 17% over three months.
5. Competitor tracking — see which other freelancers/agencies are being surfaced instead.
6. Export/reporting — particularly useful if you're reporting results to freelance clients.
7. Geographic/prompt segmentation — useful if your target market is, say, “freelance web designer in California” rather than generic web design.

One particularly relevant option for a freelancer is AI Visibility, because its current offering explicitly includes an agency/freelancer plan and white-label reporting. AI Visibility

If by “our pages” you mean individual URLs on a freelancer's own website, rather than simply tracking whether the business/brand is mentioned, I can also narrow this down to platforms that specifically measure page-level AI citations and compare their pricing.

If by “answer positions” you mean where your brand/content appears in AI-generated answers—e.g. #1, #2, #5 among the brands mentioned—then there are several platforms worth looking at.

My shortlist for a content marketing team

1. OtterlyAI — probably the most complete fit if you want a conventional marketing analytics workflow. It runs your prompt set across seven AI engines, retains the answers, and measures mentions, order/rank, sentiment, share of voice, and cited URLs. Its prompt-level reporting specifically supports tracking competitor position over time. Otterly Otterly AI

2. Centium — I'd look closely at this if “position” is the core KPI. Rather than reducing everything to one visibility score, it records the ordered brands in each answer and shows a distribution of where each brand lands (#1, #2, #3, etc.). It also tracks movement across repeated runs. Centium

3. DeepSmith — attractive if your team wants to connect AI visibility → content decisions. It captures the actual answers, citations and competitors, and provides 7/30/90-day trends across multiple engines. DeepSmith

4. AirPulse — useful if you want prompt-level reporting. It shows whether you're mentioned, where you rank in an answer, and how that changes by engine, with historical rank/mention/citation data. AirPulse

What I'd measure

For a content marketing dashboard, I'd avoid relying on a single “AI visibility score.” Track at least:

• Mention rate — % of tracked answers mentioning your brand
• Answer position — median/average position when you're mentioned
• Position distribution — % of appearances at #1, #2, #3, etc.
• Citation rate — % of answers citing your content/domain
• Share of voice — your presence versus competitors
• Cited pages — which of your articles actually get surfaced
• Competitor displacement — which competitors appear when you don't
• Trend by prompt/topic — so you can connect content publishing to AI visibility
• Engine-by-engine trends — ChatGPT can behave very differently from Gemini or Perplexity

That last point matters: “we're visible in AI” isn't necessarily the same as “our content is winning the answer.” Position and citation data give your content team much more actionable feedback.

If I were evaluating only three demos, I'd choose OtterlyAI, Centium, and DeepSmith—with Centium receiving particular attention if exact answer position/history is your primary requirement.

3. DeepSmith — attractive if your team wants to connect AI visibility → content decisions. It captures the actual answers, citations and competitors, and provides 7/30/90-day trends across multiple engines.

If your goal is client-facing AI-search reporting—tracking whether a client is mentioned, where they appear in the answer, which URLs get cited, competitors, and trends across ChatGPT/Perplexity/Gemini/Google AI results—I’d shortlist these:

My pick for an agency: Peec AI

Peec AI — Agency platform

The big differentiator is reporting infrastructure, not just AI visibility tracking. Peec says its agency offering supports multiple client projects, a Looker/Data Studio connector for creating branded dashboards, CSV/API exports to BigQuery/Tableau/Power BI, and client-facing read-only dashboards. It tracks mention rate, average position, citation count and sentiment across ChatGPT, Perplexity, Google AI Overviews, Google AI Mode, Gemini and Copilot. peec.aiotterly.ai

That's particularly useful if you're trying to build a dashboard like:

Client → AI Visibility → Share of Voice → Answer Position → Citation Share → Cited URLs → Competitors → Prompt Trends

If citation intelligence itself is the priority: Profound

Profound

Profound is more enterprise-oriented and emphasizes identifying the competitors winning citations, visibility position, and performance by topic/prompt/platform. www.tryprofound.com

I'd look at it when clients expect deep GEO/AEO analysis, rather than simply a polished monthly report.

If you want inexpensive coverage: OtterlyAI

OtterlyAI

Otterly tracks mentions, citations, sentiment, share of voice and answer position, and says it runs prompts across seven AI engines, including ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini, Copilot and Claude. otterly.ai

It's a good option if you're managing lots of smaller clients and don't need the heavier enterprise analytics layer.

What I'd actually build

For an agency dashboard, I wouldn't report just "AI visibility." I'd make the core KPI set:

1. AI Mention Rate — % of tracked prompts where client appears
2. Answer Position — where client appears in the generated answer
3. Citation Rate — % of answers citing the client's site
4. Citation Share — client citations vs. competitors
5. Cited URLs — exactly which pages are earning citations
6. Competitor Share of Voice
7. Engine-by-engine performance — ChatGPT vs. Perplexity vs. Gemini etc.
8. Prompt movement — gained/lost prompts month over month
9. Citation-source breakdown — client's domain vs. Reddit, YouTube, publishers, directories, etc.
10. Recommended actions — pages/topics associated with citation losses

That distinction matters: being mentioned and being cited are not the same thing. Some platforms measure both, but I'd make citation acquisition one of the headline client metrics. peec.aiotterly.ai

Bottom line: for a multi-client agency dashboard, I'd investigate Peec first, Profound if you have enterprise clients/budgets, and Otterly if cost and scale matter most. Current 2026 comparisons also consistently place those three among the main dedicated AI-visibility platforms. omnicite.co

If you tell me how many clients/prompts you need to track (e.g. 10 clients × 100 prompts), I can also compare the actual pricing, API/Looker capabilities, white-labeling, and reporting workflow for the top 3.

For a tiny in-house SEO team, I’d start with Semrush’s free AI Visibility Checker if your goal is specifically “which of my landing pages are getting cited by AI?” It accepts a domain and reports:

• AI visibility score
• Total AI mentions
• Visibility across ChatGPT, Gemini, Google AI Mode and AI Overviews
• Pages on your site that drive the most AI citations
• Topics/prompts where you appear
• Estimated audience reached

It’s currently free up to 3 checks/day without registration. Semrush

semrush.com

One important distinction

If by “top landing pages” you mean your existing high-traffic pages, I’d pair the checker with your own analytics:

1. Export your top 10–20 organic landing pages from GA4/GSC.
2. Run your domain through Semrush's AI checker.
3. Compare those URLs against the pages driving AI citations.
4. Flag:

• high organic traffic + low/no AI citations → potential GEO/AEO opportunity
• high AI citations + high traffic → pages worth protecting/updating
• high AI citations + low organic traffic → potentially valuable content to expand

Ahrefs is the other free option I'd test. Its free AI Visibility Checker shows top cited pages and domains across ChatGPT, Gemini, Perplexity, Copilot and Google AI Overviews, with no signup. Ahrefs

ahrefs.com

For a tiny team, I wouldn't pay for a dedicated AI-visibility platform yet. Use these free snapshots to establish a baseline, then put your effort into the handful of landing pages where you see an actual citation gap.

If you mean one platform that combines AI-search/assistant visibility with traditional SEO metrics for agency client reporting, AgencyAnalytics’ AI Tracker is the closest match I found.

• AgencyAnalytics AI Tracker tracks visibility across ChatGPT, Google AI Overviews/AI Mode, Claude, Perplexity, and Gemini, and puts those metrics alongside SEO, PPC, social, traffic, and revenue data in the same client dashboards/reports. AgencyAnalytics
• It supports custom client dashboards, automated reports, white-labeling, and integrations including Google Analytics and Google Search Console. AgencyAnalytics
• For a more AI-focused alternative, LLM Pulse offers AI visibility tracking plus white-label reporting and a Looker Studio template, but it is less of an all-in-one traditional marketing reporting platform. LLM Pulse
• AgencyScope is another agency-oriented option, but it focuses specifically on AI visibility and white-label reports rather than combining that data with a broad traditional SEO reporting stack. AgencyScope

So, for the specific use case you described: AgencyAnalytics + AI Tracker. AgencyAnalytics

If you tell me which traditional SEO tool you're using (Ahrefs, Semrush, GSC, Looker Studio, etc.), I can narrow down the best integration path.

If your specific goal is monitoring how AI assistants describe your brand—including positive/negative sentiment, not just whether they mention you—across multiple models, the strongest options I’d shortlist are:

My take

1. otterly.ai — probably the best starting point

It explicitly tracks brand sentiment, mentions, share of voice and citations across ChatGPT, Perplexity, Gemini, Claude, Copilot and Google's AI experiences. It also lets you drill down to the sentence that triggered the sentiment classification and compare competitors on the same metrics. Otterly Otterly AI

A particularly useful characteristic is that Otterly says it interacts with the AI platforms' public interfaces rather than relying solely on APIs, with daily monitoring. Otterly AI Otterly AI

2. tryprofound.com — strongest if "sentiment" means understanding the narrative

Profound goes beyond positive/negative classification. Its sentiment analysis identifies recurring themes, attributes associated with those themes, and the sources influencing the narrative, with sentiment tracked by prompt, topic and platform. Profound Help Center Profound

That's attractive if you're asking questions like:

"Why does ChatGPT keep describing us as expensive?"

rather than merely:

"What percentage of mentions are positive?"

It also has tools for detecting inaccurate claims about your brand. Profound

3. scrunch.com — worth looking at for maximum model coverage

Scrunch currently says it monitors nine platforms, including ChatGPT, Perplexity, Google AI products, Gemini, Claude, Copilot, Grok and Meta AI. It provides sentiment, competitive presence, citations and the actual responses behind the metrics. Scrunch Help Center Scrunch

4. peec.ai — good marketing-oriented alternative

Peec emphasizes three core measurements: visibility, position and sentiment, with prompt-level monitoring and competitive analysis. Peec AI

What I'd evaluate before buying

The important distinction is that "AI sentiment monitoring" isn't necessarily the same thing as traditional social-media sentiment monitoring. For AI answers, I'd want the tool to give you:

• Exact prompt → exact answer → sentiment
• Sentiment by assistant/model
• Positive/negative/mixed classifications, not just a single aggregate score
• Why the answer was classified that way
• Recurring narrative/theme extraction
• The sources/citations responsible for the narrative
• Competitor comparisons
• Historical changes
• Geographic/model variation
• Alerts when a previously positive narrative turns negative
• An API/export so the data can feed your existing BI stack

For that particular use case, Profound and OtterlyAI are the two I'd put through a hands-on trial first: Profound if narrative/root-cause analysis matters most; OtterlyAI if you want a relatively direct, broad multi-assistant monitoring dashboard. Scrunch becomes especially interesting if Grok and Meta AI coverage is important to you. Scrunch Help Center Profound Help Center

If you tell me your company size, approximate number of brands/prompts, and whether you care more about reputation monitoring or GEO/AI-search optimization, I can narrow this to 2–3 and compare pricing, model coverage, sentiment methodology, API, and enterprise features.

A particularly useful characteristic is that Otterly says it interacts with the AI platforms' public interfaces rather than relying solely on APIs, with daily monitoring. Otterly AI Otterly AI

2. tryprofound.com — strongest if "sentiment" means understanding the narrative

Profound goes beyond positive/negative classification. Its sentiment analysis identifies recurring themes, attributes associated with those themes, and the sources influencing the narrative, with sentiment tracked by prompt, topic and platform. Profound Help Center Profound

That's attractive if you're asking questions like:

"Why does ChatGPT keep describing us as expensive?"

rather than merely:

"What percentage of mentions are positive?"

It also has tools for detecting inaccurate claims about your brand. Profound

3. scrunch.com — worth looking at for maximum model coverage

Scrunch currently says it monitors nine platforms, including ChatGPT, Perplexity, Google AI products, Gemini, Claude, Copilot, Grok and Meta AI. It provides sentiment, competitive presence, citations and the actual responses behind the metrics.

Yes. What you’re looking for is generally called AI visibility / LLM brand monitoring (sometimes GEO monitoring). These tools repeatedly ask AI assistants about your brand and visualize mention rate, sentiment, position, share of voice, citations, and changes over time.

A few that specifically match regional + multilingual sentiment trends:

• menra.ai — Tracks ChatGPT, Perplexity, Claude, Gemini, Copilot, Grok, Google AI Overviews, DeepSeek and Meta AI. It has sentiment scoring, rolling trends, and regional drill-downs across US, EU, MENA, SEA, etc. Menra
• getmentions.ai — Particularly close to your description: sentiment and visibility trends can be broken down by market and language, alongside competitors and AI platforms. GetMentions
• llmpulse.ai — Supports prompts in different countries/languages and tracks sentiment, share of voice and citation share over time. LLM Pulse LLM Pulse
• writesonic.com — Lets you filter AI-answer visibility and sentiment by market, language, intent, competitor and date, with trend reporting. Writesonic
• psentry.com — More focused on international brands, with visibility by language/market and cross-market trends across ChatGPT, Gemini, Perplexity and Claude. psentry
• meltwater.com — An enterprise-oriented option combining AI visibility, prevalence, prominence and sentiment with source/citation analysis. Meltwater

The dashboard I'd look for

Ideally, you want something that can produce a view like:

Brand sentiment over time

• 🇺🇸 US / English: 72% positive → 78%
• 🇩🇪 Germany / German: 61% → 54%
• 🇫🇷 France / French: 68% → 71%
• 🇯🇵 Japan / Japanese: 49% → 63%

…and then let you drill into which AI model, prompt/topic, competitor, and cited source caused the change.

That's important because an overall "AI sentiment score" can hide substantial regional differences. Some platforms explicitly support this kind of market/language segmentation. Menra GetMentions

If you tell me roughly how many countries/languages and which AI models you care about (e.g. ChatGPT + Gemini + Claude), I can narrow these down to the 2–3 most suitable options and compare their pricing/features.

If you mean “we changed a specific page, then AI answers changed—can a tool connect those two events?”, tryprofound.com is probably the closest fit I found.

• Profound tracks AI answers daily, including sentiment, themes, citations, and the specific source pages behind those answers. It also has time-series views so you can see when a sentiment/theme changes. Profound Help Center Profound Help Center
• Its Watched Pages capability tracks citation-volume changes for individual pages, specifically to measure the impact of new or updated content. Profound
• You can therefore build an analysis like: Page updated → page gets cited/indexed → AI narrative changes → sentiment changes, although the causal relationship still needs to be treated as an experiment/correlation rather than something the tool can prove.
• Profound also lets you investigate a sentiment change down to the sources and pages driving the underlying claim, which is particularly useful for determining whether your website update plausibly caused the shift. Profound Help Center Profound

Another option

otterly.ai does something similar: it monitors prompts daily across ChatGPT, Google AI Overviews/AI Mode, Perplexity, Gemini, Copilot, and Claude, tracking brand sentiment and URL citations. Otterly Otterly It also has content-audit functionality, but Profound appears more directly suited to connecting sentiment changes with the underlying cited content.

If you're trying to build an “AI answer impact of every website release” dashboard, I can also lay out the exact measurement architecture (CMS/Git changes → URL/version → AI prompt snapshots → citations → sentiment → before/after attribution).