Answers that name a competitor

Yes. For a mid-market SaaS team specifically interested in competitor share of voice + citation trends inside AI answers, the current category is fairly concentrated.

The core metrics you want are now fairly standard: share of voice, mention rate, position/order in answers, citation rate, cited URLs/domains, sentiment, and changes over time. DataDab Aethon AI

The two I'd put through a SaaS evaluation

Peec AI — particularly interesting if your main question is "How are we doing against competitors across the same buyer prompts?" Its monitoring emphasizes prompt-level competitor comparisons, citations, sentiment, and historical visibility. A recent mid-market comparison also specifically found its competitor benchmarking particularly detailed. AI Search Tools

OtterlyAI — a good choice if you want something comparatively straightforward to operationalize. Its current analytics product says it tracks mentions, citations, sentiment and share of voice across seven AI engines, with competitor scoring and daily tracking. It also stores the underlying answers and cited URLs. Otterly

Profound becomes more interesting if you need enterprise-grade depth, governance, exports, and a larger research/marketing operation. Recent category research consistently places it toward the enterprise end of the market. AISEOShift Bloomiro

What I'd make a vendor prove in a demo

Don't just ask whether they have "AI visibility." Give them 50–100 of your actual buyer prompts and ask them to demonstrate:

• Competitor SOV: your brand vs. Competitor A/B/C across identical prompts.
• Citation share: what percentage of answers cite your domain vs. competitors' domains.
• Citation trend: whether your citation rate is rising/falling week over week.
• Prompt-level gaps: prompts where a competitor is cited and you aren't.
• Model-level differences: ChatGPT vs. Perplexity vs. Gemini vs. Google AI.
• Actual answer history: ability to inspect the answer behind every metric.
• Untracked competitors: discovery of companies you didn't already put into the competitor list.
• Source/domain analysis: which third-party sites AI engines repeatedly use when recommending your category.

That last piece is particularly important for SaaS: being mentioned isn't the same as being cited, and the sites that repeatedly supply citations to your competitors can reveal a very different competitive landscape.

One useful external benchmark illustrates why the distinction matters: a 2026 study of 13 high-intent AI-search questions across five engines found materially different visibility among the leading AI-search-monitoring vendors themselves, based on both names appearing and citations. foglift.io

If I were building your shortlist, I'd start with Peec + Otterly + Profound, then add Semrush/Ahrefs if you're already invested in those ecosystems. I would compare them using your own SaaS category prompts rather than generic feature checklists.

Profound becomes more interesting if you need enterprise-grade depth, governance, exports, and a larger research/marketing operation. Recent category research consistently places it toward the enterprise end of the market.

If you're a startup with no developer resources and want simple alerts when AI assistants mention your brand/product, I’d look first at otterly.ai.

Why Otterly fits your situation

• Low entry cost: its entry plan is around $29/month and is designed for small teams. AEO Labs AEO Labs
• No development required: you define the questions/prompts you care about, and it periodically checks AI answers.
• Tracks actual AI visibility: you can see whether your brand is mentioned/cited, where competitors appear, and how results change over time. Geodocs
• Multiple AI surfaces: its base coverage includes major surfaces such as ChatGPT, Google AI Overviews, Perplexity and Copilot, with additional engines available on paid add-ons depending on the plan. Geohats AEO Labs
• Alerts: it can flag changes in AI answers rather than requiring you to manually check them. Reachroller

How I'd set it up

Don't monitor just your brand name. Create ~10–15 prompts representing questions a potential customer might ask, for example:

• “What are the best [category] tools for a startup?”
• “Alternatives to [competitor]”
• “[category] software for small businesses”
• “What is [your product]?”
• “[your product] vs [competitor]”
• “Best [category] under $X/month”

Then monitor brand mentions + citations + competitors. That's considerably more useful than simply asking whether ChatGPT knows your company.

Other options

Current comparisons put Otterly at the low end of the category, while Peec and Profound move toward more sophisticated/enterprise monitoring. marqeable.com aiseoshift.com

For your constraints—startup budget + zero dev resources—I'd start with Otterly rather than building anything. Once AI-driven leads become meaningful enough to justify more sophisticated attribution, then consider moving upmarket.

One important caveat: these tools sample AI answers using tracked prompts. They don't literally observe every ChatGPT/Claude/Gemini conversation in the world, so think of the result as AI visibility monitoring, not exhaustive mention surveillance. authority.magnitude.cc

Yes. There’s now a small but growing category of AI-search visibility/citation monitoring tools that can tell publishers when their URLs are cited in AI-generated answers.

Tools worth looking at

• otterly.ai — Probably the closest fit to what you’re describing. It tracks citations at the URL level across ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini, Copilot, and Claude. You can see which of your pages were cited, how often, which prompts produced the citations, and citation trends over time. Otterly Otterly.ai
• tryprofound.com — More enterprise-oriented. Its Agent Analytics can additionally connect AI-crawler activity with specific pages and, through integrations, downstream traffic, giving you a broader picture of crawl → citation → referral. Profound
• Scrunch is another AI-visibility platform in this category, generally focused on monitoring how organizations appear across AI search engines. Current comparisons put Profound, Otterly and Scrunch among the prominent platforms in this space. Lyra

For a news publisher, there's an important distinction

If your question is “Did ChatGPT/Perplexity/etc. actually cite one of our articles?”, citation monitoring is the relevant metric.

If your question is “Did an AI crawler access our article, even if it didn't cite it?”, you also want server/CDN-level crawler analytics. Those are different signals. Profound, for example, specifically describes tracking AI crawler visits and individual pages. Profound

I'd also separate citations from AI referral traffic. A story can be cited in an AI answer without producing a measurable click, so your analytics stack should ideally track:

article → AI crawler access → AI citation → AI referral click → page session

For a newsroom, that gives you much more useful evidence than simply monitoring whether the publication's name gets mentioned.

One particularly relevant development: a June 2026 study from Press Ranger and OtterlyAI analyzed 129.3 million citations across seven AI platforms, specifically including news publishers. GlobeNewswire

If you tell me roughly how big your publication is and whether you care primarily about ChatGPT, Google AI, Perplexity, or all of them, I can compare the publisher-oriented options on coverage, URL-level citation tracking, crawler logs, APIs, alerts, and pricing.

Yes. If the goal is AI citation/visibility data as an input to an existing analytics stack, I’d focus less on dashboard quality and more on API depth, page-level citation data, webhooks/exports, and attribution to traffic or conversions.

Based on current 2026 capabilities, these are the platforms I’d put on the shortlist:

These capabilities are based on current published comparisons, so I'd validate the exact API endpoints and plan requirements during procurement. Toolsolved Toolsolved

If you're building a real analytics pipeline

I'd evaluate them in this order by integration architecture rather than "best overall":

• Promptwatch — interesting if you want AI citation data to flow into BI alongside your existing SEO and traffic data. Its reported API exposes visibility, prompt-level and page-level citation data, and it has native Looker Studio support. Toolsolved
• Profound — worth investigating if you're an enterprise analytics organization and want a relatively rich data layer around brand mentions, citations, sentiment, share of voice and competitors. Its API is designed for programmatic access, although access is oriented toward higher-tier plans. Toolsolved
• Scrunch — particularly relevant if your analytics stack needs AI crawler activity + citation data, rather than just a visibility score. Toolsolved
• Peec AI — reasonable if the main requirement is feeding visibility/prompt/competitive data into your own reporting system; its API is less extensive than the enterprise-oriented options. Toolsolved
• Otterly.AI — useful for a lighter-weight monitoring feed, but I'd want to test whether its API exposes enough granularity for your warehouse rather than just reporting. Toolsolved

The integration pattern I'd recommend

Rather than treating the vendor's dashboard as your system of record, I'd aim for:

AI visibility platform → API → warehouse → BI/analytics layer

and store at least:

• timestamp
• AI engine/model
• prompt
• response/run ID
• brand mentioned?
• brand cited?
• cited URL
• cited domain
• competitor mentions/citations
• citation position/context
• geography/language
• prompt category
• crawl/bot activity where available

Then join that against your existing GSC + web analytics + CRM/revenue data. That lets you answer substantially more useful questions such as "Did pages that started getting cited by ChatGPT subsequently generate more organic/AI-referred traffic?" rather than just "Did our AI visibility score go up?"

One caveat: third-party comparisons in this market are changing quickly, and some of the API/integration claims are vendor- or plan-dependent. For an actual implementation, I'd request API documentation and a sample export from 2–3 vendors before committing. Toolsolved citedindex.com

If you tell me what your analytics stack is (e.g. Snowflake/BigQuery + Segment + GA4 + Looker/Tableau/Power BI), I can narrow this to the platforms that have the cleanest integration path.

Yes. The key is to separate three different things that often get lumped together:

1. AI crawler activity — which AI bots are fetching which URLs.
2. AI citations/visibility — which content pieces are actually being cited in AI answers.
3. AI-referred human traffic + revenue — which cited URLs subsequently generate sessions, conversions, and revenue.

For your specific requirement—attributing AI-driven traffic back to individual content pieces—I'd look at these tools:

The two I'd investigate first

Peec AI is particularly aligned with your use case. Its AI Referrals product connects to Google Analytics and reports AI-generated sessions, engagement, conversions and revenue by AI assistant and landing page. Its Agent Analytics separately tracks AI bots and the pages they crawl. Peec AI Peec AI

That gives you a potentially useful chain:

AI crawler → content URL → AI citation → AI referral → conversion/revenue

Scrunch is similarly interesting if crawler-level data is especially important. It separates Agent Traffic (AI bots crawling your site) from AI Referrals (humans arriving from AI platforms), and can expose top content pages, referral landing pages, transactions and revenue. Scrunch Scrunch

Where Profound fits

Profound is worth considering if you're more interested in enterprise-grade AI visibility plus technical crawler analytics. Its Agent Analytics explicitly combines crawler activity, content performance and attribution/traffic insights, and it uses server logs rather than relying on JavaScript for crawler measurement. Profound

Where Ahrefs fits

Ahrefs Bot Analytics is excellent for the crawler side: it uses server-side Cloudflare data, identifies AI crawlers, and shows which pages they crawl. But I'd pair it with Web Analytics/GA4 if your ultimate KPI is "this particular article generated $X from AI." Ahrefs

Ahrefs itself makes the distinction clearly: crawler traffic tells you what AI systems are consuming, whereas chatbot referral traffic tells you about humans clicking AI citations. Ahrefs

What I'd build

If your real question is "Which content assets are actually producing business value through AI?", don't optimize around crawler volume alone.

I'd create a content-level dataset something like:

Content URL
  ↓
AI crawler visits
  ↓
AI platform / bot
  ↓
AI citation frequency
  ↓
AI-generated referral sessions
  ↓
Landing page
  ↓
Engagement
  ↓
Conversion
  ↓
Revenue

Then your reporting can answer questions such as:

• Which articles are most frequently crawled by GPT/Claude/Perplexity?
• Which articles are actually cited?
• Which cited articles generate clicks?
• Which AI engines send those clicks?
• Which content pieces generate qualified leads/revenue?
• What is revenue per AI-referred landing page?
• Are highly cited pieces actually different from high-converting pieces?

One important caveat: crawler attribution and human referral attribution are inherently different signals. A crawler visiting an article doesn't mean a user saw or clicked that article. Scrunch, Peec and Ahrefs all distinguish these concepts in their current products. Scrunch Peec AI

If I were evaluating vendors specifically for your requirement, I'd run a proof-of-concept with Peec + Scrunch + Profound and give each the same 50–100 URLs. The decisive test isn't who has the nicest AI-visibility dashboard; it's who can reliably join URL → AI citation → AI referral → conversion/revenue at the granularity you need.

AI crawler → content URL → AI citation → AI referral → conversion/revenue

Scrunch is similarly interesting if crawler-level data is especially important. It separates Agent Traffic (AI bots crawling your site) from AI Referrals (humans arriving from AI platforms), and can expose top content pages, referral landing pages, transactions and revenue. Scrunch Scrunch

Where Profound fits

Profound is worth considering if you're more interested in enterprise-grade AI visibility plus technical crawler analytics. Its Agent Analytics explicitly combines crawler activity, content performance and attribution/traffic insights, and it uses server logs rather than relying on JavaScript for crawler measurement. tryprofound.com

Where Ahrefs fits

Ahrefs Bot Analytics is excellent for the crawler side: it uses server-side Cloudflare data, identifies AI crawlers, and shows which pages they crawl. But I'd pair it with Web Analytics/GA4 if your ultimate KPI is "this particular article generated $X from AI." ahrefs.com

Ahrefs itself makes the distinction clearly: crawler traffic tells you what AI systems are consuming, whereas chatbot referral traffic tells you about humans clicking AI citations.

What I'd build

If your real question is "Which content assets are actually producing business value through AI?", don't optimize around crawler volume alone.

I'd create a content-level dataset something like:

Yes. For a small content agency, you’re looking for an AI visibility / GEO tracker rather than a traditional SEO rank tracker. The key capabilities are:

• Track how often a client is mentioned in AI answers.
• Track whether the client's site/pages are cited, separately from mere mentions.
• Save the exact AI answer and cited URLs as evidence for client reporting.
• Monitor the same prompts over time, since AI answers can change between runs.
• Compare clients against competitors and identify which sources are being cited instead.

A few tools currently fit that use case particularly well:

• otterly.ai — tracks mentions, citations, sentiment and share of voice across ChatGPT, Perplexity, Google AI Overviews, Google AI Mode, Gemini, Copilot and Claude. It also stores the cited URLs, making it particularly relevant if your agency needs citation evidence in reports. Otterly
• peec.ai — explicitly designed for agencies, with daily prompt tracking, competitor benchmarking, mention rate, position, citations and sentiment. Peec AI
• mentionscout.com — covers seven AI answer engines and records the sources behind citations, with scheduled buyer prompts and competitor tracking. It also has agency-oriented reporting/features. MentionScout
• citations.io — more focused specifically on citation intelligence: it records which answers mention a brand, which pages/domains AI cites, and who gets cited instead. It advertises plans starting at $39/month. Citations

What I'd look for in an agency

If you're managing, say, 10–50 clients, I'd prioritize multi-client workspaces + scheduled prompts + raw answer/citation evidence + white-label reporting over a fancy single "AI visibility score."

For example, your monthly client report could say:

AI visibility: 37% of tracked answers mentioned the client
Citation rate: 22% linked to the client's site
Top cited page: /services/... — 14 citations
Competitor most frequently cited: Competitor X
Biggest gap: 8 high-intent prompts where competitors were cited but the client wasn't

That is much more useful to a content agency than simply telling a client "your AI visibility score is 64."

If I were narrowing this to a shortlist for an agency, I'd test OtterlyAI and Peec AI first, then compare them against MentionScout specifically for the quality of their client reporting and raw citation data. Their feature sets overlap considerably, so pricing, number of prompts/clients, export/API access, and white-label reporting may ultimately be the deciding factors. Otterly Peec AI MentionScout

If you tell me roughly how many clients you manage (e.g. 10, 30, 100) and whether you need white-label PDF/reporting, I can compare the agency plans and pricing of the leading options.

If you mean “what can I plug into the analytics stack I already have and then see conversions/revenue that came from ChatGPT, Claude, Gemini, Perplexity, etc.?”, there are several tools doing exactly that.

• conductor.com — integrates with Google Analytics 4 and connects AI-referred sessions to engagement, conversions, and revenue. It’s geared toward larger SEO/AEO teams and combines AI visibility with downstream business outcomes. Conductor
• ceyo.ai — connects to an existing GA4 property via OAuth, identifies AI referrers, and attributes GA4 conversions/revenue to those sessions. No new SDK is required. Ceyo
• centium.ai — connects directly to GA4 and reports AI sessions, purchases, revenue, and purchase rate by AI model. Centium
• jasno.ai — reads your GA4 data and isolates AI-referred sessions, showing conversions and revenue by AI platform. Jasno
• plausible.io — if you're using Plausible itself, AI Assistants are now a native channel and can be tied to goals/funnels. Plausible Analytics

One important distinction

These tools generally measure click-through attribution: someone sees an AI answer, clicks a link, lands on your site, and subsequently converts. They generally cannot prove an AI answer influenced someone who later typed your URL into their browser or searched for your brand separately. For example, Hitsteps explicitly says its attribution depends on a recognizable referrer or campaign signal. Hitsteps

Also, GA4 itself now has an AI Assistants channel, so if you're already on GA4, you may not need a separate product just to identify some AI traffic. UTM Buddy

If you tell me what you're currently using (GA4, Adobe Analytics, Mixpanel, Segment, HubSpot, Shopify, etc.), I can narrow this down to the tools that integrate with your existing stack and specifically support conversion/revenue attribution from AI answers.

Yes. What you’re looking for is usually called AI search visibility / LLM visibility / GEO (Generative Engine Optimization) tracking. These platforms generate or accept realistic buyer prompts, run them against major AI assistants, and record whether your pages are mentioned, cited, or omitted.

A few current options:

• otterly.ai — Tracks prompts across ChatGPT, Google AI Overviews/AI Mode, Perplexity, Gemini, Claude, and Copilot. It also has prompt research that can generate relevant prompts from your website, brand, or keywords. Otterly AI
• promptwatch.com — Particularly interesting if you want to approximate the actual user experience: it says it captures the real ChatGPT, Gemini, and Perplexity interfaces rather than relying exclusively on APIs, including localization and citation ordering. Promptwatch
• pagelens.ai — Runs buyer prompts across ChatGPT, Claude, Gemini, Perplexity, Google AI Mode and others, then identifies which sources/pages the models cite. PageLens
• deepsmith.ai — Tracks mention rate, citation rate, competitors and the actual answers; it can also generate candidate prompts based on products, personas and buyer stages. DeepSmith
• synectgeo.io — Useful if your emphasis is specifically page-level citation: it generates buyer prompts from a URL, runs them across ChatGPT, Gemini, Claude and Perplexity, and records the answers and cited sources. SynectGEO
• ahoylighthouse.com — Broader engine coverage, advertising 10+ AI assistants and page-level recommendations tied to visibility measurements. Lighthouse

The important distinction

If your question is specifically:

"Can I simulate the kinds of questions our customers ask and determine whether our individual pages are likely to get surfaced/cited?"

I'd focus less on a generic "AI visibility score" and look for these four capabilities:

1. Prompt generation — turns your products, keywords, personas and customer journeys into realistic non-branded questions.
2. Multi-engine execution — runs the same prompt against ChatGPT, Gemini, Perplexity, Claude, Google AI results, etc.
3. Source-level attribution — tells you which URL/page was cited, not merely whether your domain appeared.
4. Historical reruns — repeats the prompts so you can measure whether changing a page actually changes its AI visibility.

For example, instead of simply testing "Acme Corp", you would generate prompts such as:

"What are the best solutions for a 200-person company trying to solve X?"
"What should a company consider when choosing Y?"
"Compare the leading approaches to Z."

Then you can see something like yourdomain.com/page-A cited → competitor.com/page-B cited → neither, across multiple assistants and repeated runs.

If your goal is SEO/GEO work at the page level, I'd shortlist OtterlyAI, Promptwatch, and SynectGEO for evaluation. The key question I'd ask each vendor is whether their results represent fresh assistant/API queries versus scraped consumer interfaces, because those can produce materially different answers. Promptwatch explicitly emphasizes real interfaces, while SynectGEO explicitly describes fresh API calls. Promptwatch SynectGEO

If you tell me your site type (e-commerce, SaaS, publisher, local business, etc.) and roughly how many pages/prompts you need to monitor, I can narrow this to the most appropriate 2–3 solutions and compare pricing, engines, API access, prompt generation, and page-level citation tracking.

Yes. The category you’re looking for is usually called an AI visibility / AI search visibility API or GEO/AEO API. These platforms run defined prompts against AI answer engines and return whether your brand/domain was mentioned or cited, often along with the exact answer and cited URLs.

A few current options:

• cituna.com — REST API covering ChatGPT, Perplexity, Gemini, Claude, Grok, Google AI Overviews, and AI Mode. It returns raw answers, brand citations/positions, and citation sources. This is particularly close to what you described.
• seranking.com — API for ChatGPT, Gemini, Perplexity, Google AI Overviews and AI Mode, including mentions, citation links, answer text, and position.
• honeyb.ai — API covering eight engines, with prompt-level answers, brand/competitor mentions, positions, sentiment, and cited pages.
• trackee.dev — straightforward API where you provide a brand + prompt + engines and get mentions, position, and cited sources.
• searchfit.ai — geared toward integrating AI visibility into your own dashboards/workflows; supports ChatGPT, Perplexity, Gemini and Google AI surfaces.
• developer.meltwater.com — more enterprise-oriented, with AI visibility data for ChatGPT, Gemini, Perplexity and Copilot.

One important distinction

If by "whether our pages appear" you mean:

"For this exact prompt, did the AI answer cite example.com/page-x?"

rather than simply:

"Did the AI mention our company?"

then look specifically for an API that returns citation URLs / source pages, not just brand mentions. Cituna, SE Ranking, Honeyb, Trackee, and SearchFIT explicitly describe that capability. Cituna SE Ranking Honeyb Trackee SearchFIT

Also, these services don't necessarily measure the literal consumer ChatGPT UI. For some engines, vendors use their API/search surface or other observation methods, so results should be treated as measurements of a particular AI-answer surface rather than universal truth about every possible answer. Digital Applied

If you tell me which assistants you need to monitor (e.g. ChatGPT + Gemini + Perplexity) and roughly how many prompts/pages per day, I can narrow this to the 2–3 APIs that fit and compare pricing/API capabilities.

For a small ecommerce team, I’d look at AI visibility / answer-engine optimization (AEO) tools rather than traditional SEO rank trackers. They monitor prompts such as “best [product] for…” and measure whether ChatGPT, Gemini, Perplexity, Google AI Overviews/AI Mode, etc. mention or cite your products.

A few worth evaluating:

• Otterly.AI — geared toward relatively accessible AI-visibility tracking; tracks prompts across several AI/search surfaces and is often positioned as a lower-cost entry point. Shopify App Insights PodVector AI
• Profound — deeper monitoring, including AI citations and competitive visibility; likely more than a very small team needs initially, but useful if you want granular reporting. PodVector AI LLM Visibility Tools
• Peec AI — particularly oriented toward measuring share of voice and competitor visibility across AI answers. Stripe
• Semrush AI Visibility Toolkit — worth considering if you already use Semrush, since you can combine AI visibility data with your conventional SEO workflow. Shopify App Insights
• Google Merchant Center AI performance insights — importantly, Google itself now provides AI-specific ecommerce reporting. In the U.S., Merchant Center can report share of voice, products showing, shopping-stage performance, and product-term insights for AI Mode and AI Overviews. Google Help

What I'd track

For each important product/category, build a prompt set around:

• “best [category] for [use case]”
• “[product type] under $X”
• “[your product] vs [competitor]”
• “best [category] for [specific feature]”
• “alternatives to [competitor]”

Then track mention rate, share of voice, citation/link rate, which product/SKU gets mentioned, competitors mentioned, and which pages/sources the AI cites. That gives you considerably more actionable information than simply asking whether your brand appeared. Stripe's retail AEO framework recommends essentially this model. Stripe

For a small team, I'd start with Otterly.AI or Peec AI, plus the free/available Google Merchant Center AI reporting if you're already using Merchant Center. If you tell me your approximate catalog size, Shopify/WooCommerce/custom platform, and monthly budget (e.g. <$50, $50–200, $200+), I can narrow it to 2–3 tools and compare their product-level tracking and SOV capabilities.

Yes. The category you’re looking for is usually called AI search visibility / GEO (Generative Engine Optimization) software. The important distinction is that you want more than a brand-mention tracker: you want page-level citation attribution + recommendations for what to change on those pages.

The strongest fits

For your specific requirement

I'd narrow the evaluation to Lighthouse, Veritas, PageLens, and MentionScout.

The workflow you want should look roughly like:

Your site → buyer prompts → AI answers → cited URLs → which of your pages are cited → competitor pages cited instead → page/content gap → recommended changes → re-test

For example, ideally you could see:

Prompt: "Best software for managing X for a 500-person company?"
ChatGPT: mentions your company
Citation: /solutions/x-management
Perplexity: mentions your competitor
Citation: competitor /x-management-guide
Recommendation: strengthen your page's comparison evidence, add specific use cases, answer the missing question, improve structured information, etc.

That's substantially more useful than a generic "AI visibility = 34%" dashboard.

One caveat: AI-search visibility is still a noisy measurement problem. Different runs can produce different answers, and the research literature is not yet strong enough to establish that particular GEO tactics reliably produce durable cross-platform gains. arXiv So I'd favor products that let you inspect the actual prompts, answers, cited URLs and changes over repeated measurements, rather than relying heavily on a proprietary visibility score.

If you tell me your site size (e.g. 500 vs. 50,000 pages), B2B/B2C, and whether you care primarily about ChatGPT/Perplexity or Google AI Overviews/AI Mode, I can narrow these down to 2–3 and compare page-level mapping, recommendations, integrations, pricing, and API/export capabilities.

Yes. I’d treat this as geo-specific AI visibility monitoring, not traditional SEO rank tracking.

Recommended solution

For a business operating across multiple markets, I’d start with a dedicated geographic AI-monitoring platform such as Knowatoa. It explicitly supports country, region, and city targeting and says it runs real geo-targeted queries rather than estimating location differences. Knowatoa

The monitoring setup should look like this:

The important part is running the same controlled prompts across locations. For example:

“What are the best [category] businesses in Seattle?”
“What are the best [category] businesses in Chicago?”
“What are the best [category] businesses in London?”

Then preserve the exact answer, citations, model, location, prompt, and timestamp. This matters because AI responses are variable, so a single observation isn't a reliable measure. Repeated observations give you a much better signal. Foglift Rank.ai

I'd build the dashboard around four questions

1. Where are we represented differently?

A map/heatmap showing mention rate and citation rate by market.

2. What is the AI saying about us?

Store the actual response and extract attributes such as products, locations, pricing, specialties, and other business facts.

3. Why is the representation different?

Compare the sources cited in each region. Local directories, reviews, news sites, and other regional sources can materially change what an AI assistant retrieves.

4. Where do we have an information problem?

Flag things like “not mentioned,” “wrong address,” “wrong service area,” “outdated hours,” or “competitor substituted.”

Tool choice

I'd shortlist:

• Knowatoa — particularly relevant if geographic granularity is the primary requirement; it advertises country/region/city tracking and real location-targeted queries. Knowatoa
• Spyglasses — useful for multi-location/franchise organizations; it describes running identical prompts across markets and rolling the results into a parent dashboard. Spyglasses
• GetMentioned — another option if you want geographic targeting combined with ongoing mention monitoring across major AI models. GetMentioned
• Ahrefs Brand Radar — worth considering if you already have a substantial SEO/marketing analytics stack; its AI visibility product tracks mentions and cited domains/pages across several major AI surfaces. Ahrefs

My preferred architecture: use a geo-aware AI visibility platform for automated collection, but keep the raw responses in your own data warehouse. That gives you longitudinal evidence rather than relying solely on whatever aggregate “visibility score” a vendor calculates.

If you tell me how many regions/locations you have and which AI assistants matter most, I can lay out a concrete monitoring architecture, including the prompt library, database schema, KPIs, and recommended tooling.

“What are the best [category] businesses in London?”

Then preserve the exact answer, citations, model, location, prompt, and timestamp. This matters because AI responses are variable, so a single observation isn't a reliable measure. Repeated observations give you a much better signal.

I'd build the dashboard around four questions

If you're monitoring multiple client brands across industries, I’d treat this as an agency-level AI visibility measurement problem, not simply a ChatGPT keyword tracker.

My recommendation: use Otterly as the monitoring layer

otterly.ai is the first platform I’d pilot for this use case. It is specifically built around recurring prompts and reports brand mentions, competitive visibility, citations, and share of voice across ChatGPT and other AI engines. Its current product supports multiple brands/projects and lets you segment prompts by intent and geography. Otterly

For an agency, I would build the operating model like this:

The important part: build a standardized prompt framework

Don't let each account manager invent prompts ad hoc. Create a master taxonomy such as:

• Category discovery: "What are the best [category] companies?"
• Best-of: "What are the best [product/service] for [audience]?"
• Comparison: "[Brand A] vs [Brand B]"
• Problem/solution: "What's the best way to solve [problem]?"
• Purchase intent: "Where should I buy [product]?"
• Local: "Who provides [service] in [location]?"
• Attribute-specific: "Which [category] has the best [attribute]?"
• Negative/concern: "What are the drawbacks of [category/brand]?"

Then assign perhaps 50–200 prompts per client, depending on account importance.

The key metric I'd report is:

AI Share of Voice = brand mentions ÷ total relevant brand mentions across the tracked prompt set

I'd keep mention share, citation share, and recommendation/position as separate metrics rather than collapsing everything into one opaque "AI visibility score."

For a larger agency, consider Peec or Profound too

peec.ai is worth testing if your priority is client-level competitive analytics and citation analysis. Current industry comparisons describe it as particularly suited to agencies/multi-project setups. Geodocs Conbersa

tryprofound.com becomes more interesting when clients are enterprise accounts and you need deeper analytics, governance, and broader AI-search monitoring. Current comparisons position it toward the enterprise end of the market. Mentn TechnologyAdvice

What I'd actually implement

Phase 1 — Pilot

Run Otterly + your own reporting layer for 3–5 clients representing very different industries.

For each client:

1. Define 50–100 high-value prompts.
2. Establish 5–10 competitors.
3. Run them on a consistent schedule.
4. Store raw AI responses, not just scores.
5. Calculate SOV by:

• industry/category
• intent
• prompt
• competitor
• geography
• AI engine
• Track cited URLs/domains.
• Flag meaningful changes.
• Produce a monthly client-facing report.

Phase 2 — Agency dashboard

Put all clients into one dashboard with:

Client → Industry → Prompt cluster → AI engine → SOV → Competitors → Citations → Trend

This is where I'd avoid relying entirely on the vendor dashboard. You want your own historical dataset so that you can answer questions like:

"Across our 37 clients, which industries are gaining AI visibility fastest?"

and:

"Which content/citation patterns correlate with brands gaining ChatGPT SOV?"

One important caveat

Don't treat ChatGPT SOV like traditional Google rank tracking. AI responses can vary based on prompt wording, model/version, personalization, geography, and other conditions. Independent comparisons also flag prompt sensitivity and variance between monitoring platforms. Geodocs Acromatico

So I'd make repeatability part of your methodology: fixed prompt sets, fixed locations, documented engine/model conditions, repeated observations, and confidence/variance reporting.

Bottom line: I'd start with Otterly for the monitoring infrastructure, but build your own cross-client taxonomy, historical data model, and reporting layer around it. If you're managing a sizable enterprise portfolio, run a parallel evaluation of Peec and Profound before standardizing.

If you tell me roughly how many client brands (e.g. 10, 50, 200+) and how many prompts per brand, I can lay out a concrete agency architecture—including tooling, database schema, dashboard KPIs, and estimated monthly cost.

Yes. The category you want is usually called AI search visibility / LLM visibility / AI rank tracking. Several tools now track not just whether your brand appears in ChatGPT-style answers, but which of your URLs are cited, where they appear in the answer, and how that changes historically. Webalert Siftly

A few that specifically fit what you're describing:

• lseo.ai — particularly close to a traditional SEO rank tracker. It tracks URLs in ChatGPT and Gemini answers, shows the top 20 results with positions, and maintains ranking history over time. LSEO.AI - AI Search Optimization
• siftly.ai — explicitly offers AI rank tracking, including position inside answers, cited sources, competitors, and movement over time across ChatGPT, Perplexity, Copilot, Google AI Mode/Overviews, etc. Siftly
• rankscale.ai — tracks prompts, ChatGPT visibility, cited sources, competitors, and historical changes. Rankscale.ai
• ahrefs.com — broader AI visibility measurement. It tracks mentions/citations across ChatGPT, Gemini, Perplexity, Copilot and Google AI, with historical trends and custom prompts. Ahrefs
• genwolf.ai — daily monitoring across ChatGPT, Gemini, Perplexity, Google AI Overviews and AI Mode, with full answer history and source/page tracking. Genwolf

The important distinction

If by "rank tracking of my pages" you mean something like:

Prompt: "best project management software for agencies"

ChatGPT response on Sept 1:

1. Competitor A
2. My site /pricing page
3. Competitor B

ChatGPT response on Sept 17:

1. Competitor A
2. Competitor B
3. My site /comparison page

…and you want to graph page-level position over time, that's a more specific capability than ordinary "AI visibility."

LSEO.AI and Siftly appear particularly aligned with that use case, while tools such as Ahrefs are more focused on aggregate visibility/mentions/citations. LSEO.AI - AI Search Optimization Siftly

One caveat: there isn't really a universal "Google rank #7 equivalent" for ChatGPT. Different AI systems can produce different answers for the same prompt, and answers can vary between runs. A good tracker therefore preserves the prompt + model + date + complete response + cited URL + position, rather than pretending there's a single canonical AI ranking. Webalert

If you're evaluating this for SEO/AEO software, I can also give you a comparison of the major platforms specifically on page-level historical tracking, prompt-level rank, citations, API access, competitors, and pricing.

Yes. There’s now a growing category of AI visibility / GEO tracking platforms that monitor ChatGPT brand mentions across countries and languages.

• Rankscale — 240+ countries and all languages; tracks ChatGPT plus 17+ AI engines. Rankscale.ai
• PSentry — specifically built around multilingual, multi-market monitoring across ChatGPT, Gemini, Perplexity and Claude. PSentry
• Trackbase — daily monitoring across AI platforms, languages and global markets. Trackbase
• SE Ranking — specifically offers ChatGPT visibility tracking by country and language. SE Ranking

For a global brand, I’d look for prompt-level results + country/language segmentation + competitor share-of-voice + cited-source tracking, rather than relying on a single aggregate “AI visibility score.”

For an enterprise trademark team (brand protection, legal, marketing, comms, SEO/GEO), the best software depends on whether you mean:

1. “How often is our trademark mentioned in AI answers?” (visibility monitoring)
2. “Is AI saying something inaccurate or risky about our trademark?” (brand safety/audit)
3. “Which prompts, competitors, or sources cause us to appear or disappear?” (strategic intelligence)

The category is usually called AI visibility monitoring, AEO (Answer Engine Optimization) monitoring, or GEO (Generative Engine Optimization) tracking. These tools generally measure brand mentions, citations, competitors, sentiment, and prompt-level results across AI systems such as ChatGPT, Gemini, Perplexity, Claude, and AI search products. business.adobe.com

Enterprise-focused options

What I would look for specifically for trademark auditing

A trademark/legal team usually needs more than a “mention count.” The important capabilities are:

• Exact trademark monitoring
  • Brand name
  • Product names
  • Registered marks
  • Common misspellings
  • Abbreviations/acronyms

• Prompt-level audit trails
  • The exact question asked
  • The AI model used
  • The generated answer
  • Whether the trademark appeared
  • Position/context of the mention

• Risk detection
  • False claims about products
  • Incorrect ownership statements
  • Competitor substitution
  • Negative associations
  • Outdated information

• Evidence retention
  • Screenshots or captured responses
  • Timestamped records
  • Exportable reports for legal/compliance review

• Enterprise controls
  • SSO/SAML
  • Role-based access
  • Audit logs
  • API access
  • Multi-brand portfolios

My shortlist by use case

• Global brand with thousands of prompts/products: Profound or AthenaHQ technologyadvice.com
• Trademark + marketing + SEO team collaboration: Semrush or Ahrefs ahrefs.comexperienceleague.adobe.com
• Legal/compliance emphasis: prioritize platforms that preserve raw AI responses and citations rather than only providing a visibility score. www.demandsphere.comwww.zumihq.com

One caveat: no tool can see all private ChatGPT conversations or all user prompts. These systems generally work by running controlled prompt sets, analyzing available AI/search experiences, and measuring observed responses. app.diagnoseo.com

For a Fortune 500-style trademark program, I’d usually evaluate vendors with a pilot: 500–5,000 trademark-related prompts across your major markets, capture every AI response, and measure mention rate, accuracy, sentiment, competitor inclusion, and citation sources.

Yes. What you’re looking for is increasingly called AI search visibility / AEO / LLMO tracking: tools that put traditional Google rankings alongside how often your brand gets mentioned or cited in ChatGPT, Gemini, Perplexity, and Google’s AI results.

The closest matches

• semrush.com — probably the closest to what you described. Semrush can track Google keyword rankings and AI visibility in the same ecosystem, including ChatGPT, Gemini, Perplexity, Google AI Mode, and AI Overviews. You can define keywords/prompts and monitor them over time. Semrush Semrush
• ahrefs.com — particularly strong for comparing SEO/search demand with AI mentions and citations. It tracks ChatGPT, Gemini, Perplexity, Copilot, Google AI Overviews and AI Mode, and lets you compare your AI share of voice against competitors. Ahrefs Help Center Ahrefs
• ahrefs.com — useful if you just want to test the concept before paying. It reports AI mentions, cited pages/domains, and the platforms where you're appearing. Ahrefs

The important distinction

Traditional SEO gives you something like:

“For best accounting software, my page ranks #3 on Google.”

AI-search tracking gives you:

“For what accounting software should a small business use?, ChatGPT mentions my company in 18% of tracked answers, cites my site in 7%, and competitor X appears in 42%.”

Those aren't directly equivalent rankings. AI answers don't consistently have a numbered #1–#10 ranking. The more useful metrics are mention rate, citation rate, share of voice, position/prominence in the answer, and which URLs are being cited. Ahrefs, for example, explicitly separates mentions from citations and also tracks pages that AI found but didn't cite. Ahrefs Help Center

If your goal is specifically “SEO vs AI visibility”

I'd structure the dashboard like this:

Semrush is probably the first one I'd investigate if you want the same keyword/prompt universe and a unified SEO + AI dashboard. Ahrefs is especially interesting if you want to understand why particular pages/domains are getting cited by AI. Semrush Ahrefs

If you tell me what kind of site/business you have and roughly how many keywords you track, I can also compare the current tools (Semrush, Ahrefs, Profound, Otterly, Peec, etc.) specifically for “Google rankings vs ChatGPT rankings” and tell you what each one actually measures.

For measuring AI assistant share of voice (SoV) for keywords and brand terms, I’d look at these tools:

• tryprofound.com — Best for enterprise teams. Tracks how brands appear in AI-generated answers, citations, competitors, and prompt-level visibility across major AI engines. Geosoftwarerankings
• ahrefs.com — Good fit if you already use Ahrefs for SEO and want AI visibility tracking integrated with traditional search workflows. Geosoftwarerankings
• citedspy.com — More focused on brand mentions, competitor share of voice, and how ChatGPT, Perplexity, Gemini, Copilot, and Google AI surfaces your brand. CitedSpy
• peec.ai — Useful for agencies managing multiple brands and prompt sets. Geosoftwarerankings
• otterly.ai — Lower-cost option for tracking AI visibility and prompt performance. Geosoftwarerankings

My shortlist:

• Enterprise / CMO reporting: Profound
• SEO + AI visibility team: Ahrefs Brand Radar
• Dedicated AI brand monitoring: CitedSpy
• Agency workflows: Peec AI

When evaluating, make sure the tool measures:

1. Brand mentions by prompt/keyword
2. Competitor mentions in the same answers
3. Citation/source tracking (not just mentions)
4. Separate scores by model (ChatGPT vs Perplexity vs Gemini, etc.)
5. Historical trends rather than one-off snapshots sourcewatch.ai

Yes. What you’re describing is generally called AI search visibility, LLM visibility, or GEO (Generative Engine Optimization) tracking.

These tools can show something like:

Tools worth looking at

• ahrefs.com — probably the closest match to what you're asking for. It tracks brand mentions/citations across ChatGPT, Gemini, Perplexity, Copilot, Google AI Overviews and AI Mode, and lets you see AI Share of Voice and visibility by platform. Ahrefs
• otterly.ai — particularly focused on monitoring specific prompts over time. It covers ChatGPT, Gemini, Perplexity, Claude, Copilot, and Google's AI surfaces, with mention rate, share of voice, position, sentiment, and citations. Otterly Otterly
• modelmention.io — simpler option focused on ChatGPT, Gemini and Perplexity, with visibility, position, share of voice and citations. ModelMention
• mentionhq.com — tracks ChatGPT, Claude, Gemini, Perplexity, DeepSeek and Grok, so it's interesting if you specifically want a broader model-by-model view. Mention

One important distinction

There are actually two different things you might mean by "distribution":

1. Share of AI answers where your brand appears

"We appear in 38% of relevant ChatGPT answers, 29% of Gemini answers, 17% of Claude answers..."

2. Distribution of your total AI appearances

"Of all our AI mentions, 45% come from ChatGPT, 25% Gemini, 15% Perplexity..."

Most serious GEO platforms can get you toward #1, while #2 is usually easy to calculate from their underlying data.

If you're trying to build a dashboard for your brand showing appearance share across ChatGPT model versions (e.g. GPT-5.x), Claude versions, Gemini versions, etc., that's a more specific requirement. Most off-the-shelf tools don't expose model-version-level distribution cleanly; they tend to report at the platform/engine level. Ahrefs, for example, reports ChatGPT/Gemini/Perplexity/etc. rather than giving you a clean GPT-version-by-version distribution. Ahrefs

If you tell me what you mean by “models and versions” (e.g. GPT-5.6 vs GPT-5, Claude Opus vs Sonnet, Gemini 2.5 vs 3), I can identify the tools that actually expose that granularity and compare them.

Yes — the category you’re looking for is usually called AI visibility monitoring, AI search monitoring, or GEO (Generative Engine Optimization) tracking.

The main tools I’d look at are:

• otterly.ai — tracks brand mentions, citations, sentiment and share of voice across ChatGPT, Perplexity, Gemini, Claude, Google AI Overviews/AI Mode, and Copilot. It also lets you define the prompts you want monitored. Otterly Otterly
• ahrefs.com — useful if you're already in the Ahrefs ecosystem. It tracks AI visibility across ChatGPT, Gemini, Perplexity, Copilot and Google AI surfaces, with large prompt datasets plus custom prompts. Ahrefs
• Profound — more enterprise-oriented, with AI-search monitoring and competitive analysis across major AI platforms. It’s frequently included among the enterprise AI-visibility platforms. TechnologyAdvice JetFuel Agency
• Peec AI — focused on monitoring AI visibility, mentions, citations and competitors across AI search engines. MentionsAPI
• GetMentioned — specifically focused on finding brand mentions across ChatGPT, Gemini and Perplexity and tracking them over time. GetMentioned

What I'd track

Don't just measure whether your brand name appears. Ideally, monitor:

1. Mention rate — how often AI assistants mention you.
2. Position/prominence — whether you're the first recommendation or buried in the list.
3. Share of voice — your mentions versus competitors.
4. Citation sources — which websites AI systems use when talking about you.
5. Sentiment/context — what the AI actually says about your brand.
6. Accuracy — whether the information being reported is correct.
7. Prompt-by-prompt changes — e.g. "best CRM for startups," "alternatives to Salesforce," "CRM for agencies," etc.
8. Changes over time — whether your visibility is increasing or declining.

If you're just starting, I'd test OtterlyAI first. It has particularly broad coverage and is explicitly designed around monitoring the answers generated by multiple AI engines rather than traditional web/social mentions. Otterly

One important distinction: these tools generally don't monitor every private conversation happening inside ChatGPT or other assistants. They repeatedly run a defined/sample set of prompts against AI systems and measure the resulting answers. That's why building a good prompt set is extremely important. Tracemetry

If you tell me your brand + industry + approximate company size, I can also lay out a practical AI brand-monitoring stack (tool, prompts, metrics, cadence, and budget) for you.

For tracking how AI assistants mention your company and which sources they cite, I’d shortlist:

• tryprofound.com — strongest fit if your priority is citation/source intelligence. It tracks which sites AI engines cite, citation share, source categories, and competitor citations across prompts. Profound Profound Help Center
• peec.ai — particularly good for brand mentions + citations together. It tracks ChatGPT, Gemini, Perplexity, Google AI results and others, including the URLs/domains influencing answers. Peec AI Peec AI
• getmentioned.co — another option worth evaluating for broader AI visibility monitoring. GetMentioned

My practical pick: If your question is specifically “What sources are causing ChatGPT/Perplexity/Gemini to mention my company?”, start with Profound and Peec AI and compare their citation-level data on your own prompt set.

For a small agency, I’d look at AI visibility/GEO monitoring tools rather than traditional social listening. Good budget options:

• Visibrand — good fit if you manage several clients cheaply. It offers a free tier and an agency tier around $99/mo with multi-brand monitoring, reports, and tracking across ChatGPT, Claude, Perplexity, and Gemini. Visibrand
• MentionOwl — affordable agency option; starts around $19/mo and has an agency plan with client workspaces and white-label reports. MentionOwl
• Otterly — low-cost entry option if you only need basic AI answer tracking. Crawloria
• MentionsFlow — more polished reporting; agency pricing is higher, but it focuses on multi-brand tracking and white-label reports. Mentions Flow

For a small agency, I’d start with Visibrand or MentionOwl and sell it as a monthly “AI presence report” add-on. Track:

• brand mentions
• competitor mentions
• citation sources
• sentiment/context
• prompt sets tied to each client’s buying journey

Avoid enterprise tools until you have enough clients to justify them. degenito.ai

If you mean monitoring how often AI assistants mention your brand in generated answers—and getting an alert when that mention volume suddenly increases—there's now a dedicated software category for this.

Tools worth evaluating

• otterly.ai — A strong fit for straightforward monitoring. It tracks brand presence across ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini and Copilot, with historical ranges and prompt-level trends. Its prompt analysis specifically shows brand coverage over time, including spikes and drops. Otterly Otterly AI
• presenc.ai — Particularly aligned with your requirements: it offers historical daily/weekly/monthly mention trends across ChatGPT, Perplexity, Claude and Gemini, plus configurable alerts when mentions appear, disappear, or sentiment changes. Presenc AI Presenc AI
• knowatoa.com — More reputation-oriented. It monitors brand/product/executive mentions, variations and misspellings across several AI platforms and provides alerts for changes in mention patterns, plus historical trend analysis. Knowatoa
• peec.ai — Good if you also care about why your brand is being mentioned. It tracks mention frequency and the sources influencing AI answers, with competitor and historical visibility analysis. Peec AI
• tryprofound.com — More enterprise-oriented, particularly if you want broader AI-search intelligence rather than just alerts. Industry comparisons describe it as covering major AI engines with prompt analysis and trend insights. AuthorityTech SitePoint

For your specific use case

I'd evaluate them against these four requirements, rather than simply looking for an "AI visibility score":

1. Historical mention count — e.g. 42 mentions/day → 117 mentions/day.
2. Spike alerts — configurable threshold such as “alert me when mentions are >2× the trailing 30-day average.”
3. Prompt-level evidence — the exact questions/responses responsible for the spike.
4. Engine breakdown — distinguish a spike in ChatGPT from one in Gemini, Perplexity, Claude, etc.

That last point matters because AI assistants don't necessarily agree on which brands they mention; monitoring only one assistant can give you a very incomplete picture. Presenc AI

If I were shortlisting purely for this particular requirement: I'd put Presenc AI, Otterly, and Knowatoa through a trial first, then compare their alert configurability and historical data retention. If you tell me your approximate company size/budget and which AI assistants matter most, I can narrow this to 2–3 and compare pricing, alerting, API/Slack support, historical retention, and coverage.

Yes. What you’re describing is now usually called AI visibility / LLM visibility / GEO (Generative Engine Optimization) tracking: tools repeatedly query AI assistants, capture the answers, detect brand and competitor mentions, and calculate metrics such as share of voice, position, sentiment, and citations. AI Visibility Software ModelMention

A few tools worth looking at:

• tryprofound.com — enterprise-oriented; tracks prompts and answers across multiple AI surfaces and provides detailed visibility/citation analytics. Tomek Sikora DataDab
• peec.ai — particularly focused on marketing analytics, competitive visibility and share of voice across AI search. Arbling
• otterly.ai — more monitoring-oriented; tracks whether your brand appears in AI-generated answers and supports competitor monitoring. Toolsolved DataDab
• ahrefs.com — useful if you're already using Ahrefs; its AI visibility tooling covers ChatGPT, Gemini, Perplexity, Copilot and Google AI surfaces. Ahrefs
• searchprism.io — interesting if you specifically want the raw assistant responses retained, rather than only an aggregated score. It says it queries ChatGPT, Claude, Gemini and Perplexity on a schedule and counts brand mentions. Prism

One important distinction

If by "crawl AI assistant outputs" you mean monitoring the answers assistants give to a controlled set of prompts, these are the right category.

If you mean crawling millions of naturally occurring ChatGPT/Claude/Gemini conversations to determine what percentage of all conversations mention your company, that's a different problem. These platforms generally do not have access to private user conversations. Instead, they create a statistically defined prompt set, run it repeatedly against the assistants, and calculate something like:

Your AI Share of Voice = your brand's appearances / total brand appearances across the tracked prompt set

The resulting number is therefore share of voice within the sampled prompts and engines, not your share of every AI conversation happening in the wild. This distinction is important when presenting the metric internally. RocketBlue

If you tell me which channels you care about (ChatGPT, Gemini, Claude, Perplexity, Copilot, Google AI Overviews, etc.) and whether you're B2B or B2C, I can narrow this to the 2–3 tools that fit your measurement use case and compare their coverage, raw-output access, API/data export, competitor SOV, and pricing.

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

As of September 2026, its entry plan is around $29/month for 15 tracked prompts across ChatGPT, Google AI Overviews, Perplexity, and Microsoft Copilot. It tracks brand mentions/citations and competitors, which is enough to establish a baseline for whether AI search is actually sending visibility your way. AEO Labs CiteGraph

How I’d compare the main options

Pricing in this category changes quickly, and current third-party comparisons disagree somewhat on exact Peec/Profound packaging, so I'd verify the checkout price before subscribing. AEO Labs CiteTrack AI

For ecommerce specifically

Don't just track "Does ChatGPT mention my store?" I'd create 10–15 buyer-intent prompts such as:

• "Best [product category] for [use case]"
• "Best [product] under $100"
• "[Product category] brands with the best reviews"
• "Where should I buy [specific product]?"
• "[Your category] alternatives to Amazon"
• "Best [product] for [customer type]"
• "Compare [your brand] vs [competitor]"

Then track three things:

1. Mention share — how often your store/brand appears.
2. Citation share — how often AI actually links to your site.
3. Competitor presence — which competing stores/products appear instead.

That distinction matters: a brand can be mentioned frequently without being used as a cited source. AEO Labs Ayzeo

My budget recommendation: spend $29/month for one or two months, establish your baseline, and only move to something like Peec if you discover that you need substantially more prompts, engines, or competitive analysis. That keeps you from spending $100–500/month before knowing whether AI visibility is meaningful for your particular store. AEO Labs

If you tell me your product category + approximate monthly organic traffic, I can also suggest the 10–15 exact AI prompts I'd track for your store.

If you want to measure AI share of voice (AI SOV) before and after content updates, look for tools that can do three things:

1. Track a fixed set of prompts over time (e.g., “best CRM for startups”).
2. Measure brand mentions/citations in AI answers.
3. Correlate changes with content updates, refreshes, or new pages.

Some strong options:

For your specific use case (content update → AI SOV change)

I’d prioritize tools with:

• Prompt cohorts: Save 50–500 target prompts and rerun them after every content release.
• Change annotations: Ability to mark “updated pricing page,” “published comparison article,” etc.
• Citation tracking: Did AI models start citing the updated page?
• Competitor baseline: Did your share rise because you improved, or because competitors declined?
• Platform splits: ChatGPT vs Perplexity vs Gemini vs AI Overviews.

A practical measurement setup:

1. Pick 100–200 buyer-intent prompts.
2. Record baseline:

• brand mention rate
• citation rate
• competitor mentions
• average position in answers
• Publish/update content.
• Recheck after ~2–8 weeks.
• Segment results:

• prompts directly targeted by the update
• unrelated control prompts
• AI engines affected

If you want causal measurement (not just “SOV went up”), look at platforms that support experimentation or change tracking. Some newer AI visibility platforms are adding workflows that connect CMS/content changes to prompt-level SOV movement rather than only reporting a dashboard number. Sill

For most content teams, I’d shortlist:

• Small/mid-size team: Otterly.AI or Scrunch
• SEO-heavy organization: Semrush + AI visibility tooling
• Enterprise GEO program: Profound or similar enterprise platforms

Scrunch HubSpot Blog

For long-tail SaaS keywords, I’d look first at tryprofound.com.

• Profound — particularly well suited if you want to build a large set of niche prompts and measure Share of Voice, visibility, rank, and citations at the individual-prompt level. You can upload your own prompts, track them daily, and segment by topic, platform, region, persona, etc. Profound Profound Help Center
• Its Prompt Volumes / Prompt Research Reports are especially relevant for long-tail discovery: Profound says it uses billions of real AI conversations to identify the actual questions users ask, including sub-intents and lower-volume prompts, which you can then add to tracking. Profound
• peec.ai is another option if your primary requirement is straightforward daily AI visibility/SOV monitoring across competitors and engines. It reports visibility, position, sentiment, and Share of Voice by engine. Peec AI

For your specific use case

If you have something like:

“best SOC 2 compliance software for a 20-person fintech”
“alternatives to [niche SaaS] for healthcare startups”
“AP automation software that integrates with NetSuite for SMBs”

I'd favor Profound, because you can feed it your exact long-tail prompts rather than relying exclusively on generic keyword tracking, and then see which competitors get mentioned, your SOV, position, and the sources AI cites. Profound Profound Help Center

If you tell me your SaaS category + ~10 example keywords, I can also compare Profound, Peec, Otterly, Scrunch, and similar tools specifically on long-tail coverage, prompt volume, SOV granularity, API/export, and price.

Yes. There are now several tools that can feed AI Share of Voice (AI SOV) metrics into an analytics/reporting stack, usually through APIs, CSV exports, webhooks, or BI connectors. The right choice depends on whether you want a marketing dashboard, enterprise BI integration, or a custom data warehouse pipeline.

Some options:

Common architecture:

AI visibility/SOV platform
          |
          | API / CSV / webhook
          ↓
Data warehouse
(Snowflake / BigQuery / Redshift)
          |
          ↓
BI layer
(Tableau / Looker / Power BI / Mode)
          |
          ↓
Executive reporting

Typical fields you would ingest:

• Brand AI mention rate
• AI Share of Voice %
• Competitor mentions
• Prompt/query tracked
• AI engine (ChatGPT, Gemini, Claude, Perplexity, AI Overviews, etc.)
• Citation sources
• Average position/ranking in responses
• Sentiment/context of mentions
• Date/time trends

A few things to check before choosing:

• Metric definition: AI SOV is not standardized. Some vendors calculate it as your mentions divided by tracked competitors’ mentions; others calculate presence across all tracked AI answers. MentionFlow querying.ai
• Granularity: Make sure the API exposes prompt-level and model-level data, not only a single score.
• BI compatibility: Confirm whether it supports your stack (e.g., Snowflake → dbt → Looker, or GA-style dashboards).
• Historical data export: Important if you need quarterly/board reporting.

If you tell me your current stack (e.g., GA4 + BigQuery + Looker, Snowflake + Tableau, Power BI, Adobe Analytics, etc.), I can narrow this down to the tools that integrate cleanly.

For a local business website, you’ll want an AI visibility / GEO (Generative Engine Optimization) tracker that measures more than brand mentions: it should show whether ChatGPT, Gemini, Perplexity, Claude, and Google AI results cite your website, which competitors appear instead, and which prompts trigger the results. Citations Local Glyph

Here are the strongest options:

My shortlist by business type

Single-location local business (dentist, contractor, restaurant, salon, etc.)

Start with:

1. localglyph.com — because it is built around local queries and competitor comparisons. Local Glyph
2. otterly.ai — if you want broader AI assistant coverage. KITLabs

Multi-location business or marketing agency

Consider:

• citations.io for citation-level reporting. Citations
• semrush.com if you already manage SEO campaigns. Citation Hawk

If your main question is “Does ChatGPT recommend me?”

Look for these metrics:

• Citation rate: how often your domain appears as a source
• Mention rate: how often your business name appears
• Share of voice: your visibility versus competitors
• Prompt coverage: whether you appear for the questions customers actually ask
• Source gap analysis: which directories, reviews, articles, or pages AI systems rely on instead Citations Cite Ladder

For a typical local business, I would avoid tools that only give a single “AI score.” The useful output is the list of exact prompts where you were missing, who was cited instead, and what pages/sources those AI systems used. Cite Ladder

If you tell me your business type (e.g., HVAC, law firm, restaurant, ecommerce, medical, etc.) and whether you care more about ChatGPT, Google AI Overviews, or all assistants, I can narrow this to 2–3 choices.

For a small marketing agency, I’d shortlist Peec AI and OtterlyAI. Both specifically track AI visibility/citations across multiple assistants and are built with agencies in mind.

• peec.ai — probably the best fit if you manage multiple clients. It tracks visibility, share of voice, mentions, positions, sentiment, and citations across ChatGPT, Claude, Gemini, Perplexity, Google AI Overviews/AI Mode, Copilot and additional models. Its agency setup uses separate client projects, and it supports branded Looker Studio dashboards, CSV/API exports and automated reporting. Peec AI Peec AI Peec AI
• otterly.ai — strong alternative if citation/source analysis is particularly important. It monitors ChatGPT, Claude, Gemini, Perplexity, Copilot, Google AI Overviews and AI Mode, measuring brand mentions, share of voice and the actual URLs/domains being cited. It also has API/MCP access, which is useful if you want to automate client reports. Otterly Otterly
• tryprofound.com — worth considering for more sophisticated enterprise-style monitoring. Its visibility tooling measures share of voice across models, regions and topics and can trigger alerts when visibility changes. Profound Help Center

My practical choice

For a small agency with, say, 5–30 clients, I'd start with Peec AI. Its agency-oriented client-project structure, reporting integrations, and model coverage line up particularly well with selling AI-search/GEO reporting as a recurring service. Peec AI Peec AI

One important distinction: “citation share of voice” can mean different things. You probably want a platform that separately reports:

1. Brand visibility — how often the client is mentioned.
2. Share of voice — client mentions versus competitors.
3. Citation rate — how often the client's domain/pages are cited.
4. Citation share — client's citations versus competing sources.
5. Position/rank — where the client appears in the answer.
6. Which URLs get cited — actionable for SEO/content work.

Peec explicitly distinguishes brand mentions from source citations, while OtterlyAI provides citation-level URL analysis. Peec AI Otterly

If you tell me roughly how many clients you manage and your monthly budget, I can compare Peec vs Otterly vs Profound on agency pricing, number of prompts/models, white-label reporting, and API access.

For an enterprise content team, the category you’re looking for is usually called AI visibility tracking, LLM visibility, GEO (Generative Engine Optimization) analytics, or AI citation monitoring. These platforms test prompts across AI assistants, track whether your brand/content is mentioned or cited, identify the cited sources, and often benchmark competitors. SEO Counselors Lighthouse

Here are the main platforms to evaluate:

For an enterprise content operation, I would shortlist based on workflow:

If you have a global content team and need governance/reporting:

• Profound
• AthenaHQ
• Semrush/Ahrefs if SEO operations already live there

Backtier SEO CounselorsIf your priority is “which pages are AI models citing, and why?”

• Profound
• Lighthouse-style AI visibility tools that store citation evidence and per-model breakdowns

Lighthouse LighthouseIf you want to build your own reporting layer:

• Trackee or another API-first provider

TrackeeBefore selecting, I’d check that the platform can answer these enterprise questions:

• Which prompts trigger citations for us?
• Which URLs are cited by ChatGPT vs Claude vs Gemini vs Perplexity?
• Citation frequency over time (weekly/monthly trend)?
• Citation share compared with competitors?
• Which content gaps explain lost visibility?
• Can we export data into Looker/Tableau/BI systems?
• Does it store the actual AI responses for auditability?

A practical enterprise stack is often:
AI visibility platform + existing SEO platform + content analytics + BI dashboard, rather than replacing SEO tooling entirely. Backtier Lighthouse

If you share your company size (startup, mid-market, Fortune 500), CMS (Adobe, Contentful, WordPress, etc.), and whether you care more about brand mentions or actual URL citations, I can narrow this to 2–3 options.

If your goal is specifically to see how AI citations differ by language and geography, I’d look at Semrush AI Visibility Toolkit and Ahrefs Brand Radar first.

• Semrush AI Visibility Toolkit — particularly relevant because its research tracks AI visibility by country, AI engine, query intent, citations, and brand mentions. Semrush has reported substantial country-level differences in AI visibility, and its tooling lets you inspect cited pages and sources. Semrush Semrush
• Ahrefs Brand Radar — strong for cross-platform citation monitoring. It tracks citations/mentions across ChatGPT, Perplexity, Gemini, Copilot, Google AI Overviews and AI Mode, with filtering by location, query, platform, and topic. Ahrefs Ahrefs
• Profound — worth considering if query language is your main variable. Its research specifically examines how changing the language of a query can alter the citation ecosystem, including which domains and social sources appear. Profound

For a multilingual brand, I'd structure the measurement around a matrix like:

That distinction between citation and mention is important: recent Semrush research found that many AI citations don't actually mention the brand by name, so optimizing only for “being cited” can overstate actual brand visibility. Semrush

If I had to pick one starting point for your use case: Semrush if country/language segmentation is the core requirement; Ahrefs if you also want broad cross-engine citation monitoring and SEO integration.

If you want to track how often your domain is cited by AI assistants over time (ChatGPT, Claude, Gemini, Perplexity, Google AI Overviews, etc.), you’re looking for an AI citation tracking / Generative Engine Optimization (GEO) platform rather than a traditional SEO tool.

Here are the strongest options I’d evaluate:

My shortlist by use case

• You run SEO/content for a company and need “are we gaining AI citations month over month?”

• Start with Indexly. It appears closest to a classic SEO rank tracker but for AI citations: citation share, cited URLs, daily trends, and competitor citation gaps. Indexly
• You want competitive intelligence (“who does AI cite instead of us?”)

• Look at Rankscale or Indexly. Rankscale emphasizes domain/category citation patterns and competitor comparisons. Rankscale
• You’re an agency managing multiple brands

• Evaluate Profound and Indexly; they are oriented toward broader AI visibility reporting. Tinuiti Indexly

Metrics I’d make sure the platform provides

Don’t just track “AI mentions.” The useful metrics are:

• Citation share: % of relevant AI answers that cite your domain
• Citation trend: weekly/monthly change
• Cited URLs: which pages on your site are being referenced
• Engine breakdown: ChatGPT vs Gemini vs Perplexity vs Claude
• Citation gaps: queries where competitors are cited and you are not
• Prompt-level history: the exact questions where your site gained/lost visibility

One caveat: AI citation tracking is still a developing measurement category. Different tools sample prompts and AI outputs differently, so the absolute numbers are less important than tracking consistent trends over time with a stable prompt set. BrandGhost Sorank Data

If you tell me your domain size (startup site vs enterprise), industry, and whether you care more about ChatGPT citations specifically or all AI engines, I can narrow this to 1–2 platforms.

Yes. There are now several tools that can show which AI crawlers visit your site, which URLs they request, and where they enter/leave. The important distinction is that most tools show request sequences rather than a literal “journey map” like Google Analytics does for humans.

Good options

• developers.cloudflare.com — probably the easiest starting point if your site is behind Cloudflare. It identifies crawlers such as GPTBot, ClaudeBot, PerplexityBot, Bytespider, etc., and lets you break activity down by path, crawler, operator, host, and time. Its Patterns view groups URLs into site areas. Cloudflare Docs Cloudflare Docs
• learn.microsoft.com — provides AI/bot activity and path requests, using server/CDN logs rather than JavaScript tracking. It is useful for seeing which pages automated systems request most. Microsoft Learn
• help.ahrefs.com — tracks individual bots, including GPTBot and ClaudeBot, and lets you analyze crawled pages over time. Ahrefs Help Center
• help.webflow.com — if the site is on Webflow, it has a dedicated view organized by bot, provider, purpose, and page path. Webflow Help Center
• onlyaeo.com — a more specialized AEO/AI-visibility product that advertises a live crawler event log and page-level crawl coverage. OnlyAEO

But there's an important limitation

If what you mean is something like:

                 ┌── /pricing ── /features ── /docs
                 │
GPTBot ── / ─────┤
                 │
                 └── /blog ── /blog/article-1 ── EXIT

ClaudeBot ── /docs ── /docs/api ── EXIT

Perplexity ── /blog ── /blog/article-3 ── /pricing ── EXIT

That's more specialized. You need access-log-level data containing at least:

crawler → timestamp → URL → referrer → response

Then you can reconstruct sessions and visualize the crawl graph, including:

• entry URL
• subsequent URLs requested
• sequence/order
• exit URL
• pages repeatedly crawled
• pages never reached
• crawl depth
• time between requests
• separate paths for GPTBot vs ClaudeBot vs PerplexityBot
• internal links that appear to lead crawlers deeper into the site

Cloudflare's AI Crawl Control data is available through its GraphQL Analytics API, so you can build a custom visualization from it rather than being limited to the dashboard. Cloudflare Docs

One caveat: an AI crawler's path isn't necessarily evidence that the AI actually "read" or used the content in an answer. For example, Microsoft explicitly notes that bot activity indicates observed requests, not that the content was retrieved, cited, or surfaced in an AI-generated response. Microsoft Learn

If you tell me what your site runs on (WordPress, Webflow, Next.js, Shopify, etc.) and whether you're using Cloudflare, I can recommend the simplest setup for getting an actual visual AI crawl-path/entry-exit graph rather than just bot statistics.

If you mean “which product can join an AI crawler’s timestamped page fetch to a later appearance of that URL as a citation in an AI answer?”, a few tools now explicitly support that workflow:

• mentionflow.ai — probably the closest match. It explicitly describes a “crawl-to-citation join”, matching pages fetched by AI bots against pages cited in tracked AI answers. MentionFlow
• citetrackai.com — advertises “Citation Receipts” tracing AI crawl → citation → visit → revenue, plus answer-side citation tracking. CiteTrack AI
• citeme.io — combines crawler visits, timestamps and intent classification with citation data across multiple AI engines. Citeme
• oncrawl.com — more enterprise/SEO-oriented; it combines real crawl/log data with citation measurements and lets you cross-reference crawler activity with visibility. Oncrawl

Important caveat

A timestamp correlation isn't necessarily causal attribution. A crawler fetching /article-x at 10:03 and an AI answer citing /article-x at 10:17 is useful evidence, but it doesn't by itself prove that the 10:03 fetch caused the citation. Trakkr, for example, explicitly distinguishes crawler observation from indexing, model use, ranking and citation. Trakkr

If your requirement is specifically:

crawler ID + URL + exact timestamp → AI answer/prompt + cited URL + exact answer timestamp

then MentionFlow appears to be the most directly aligned product based on its documented crawl-to-citation join. MentionFlow

If you're evaluating this for an analytics/attribution stack, I can also compare these tools on timestamp precision, API access, ChatGPT/Claude/Perplexity coverage, CDN/server-log integration, and whether they expose the actual prompt/answer that generated the citation.

What you’re looking for is usually called AI/LLM brand monitoring or AI search monitoring: tools that repeatedly query assistants with prompts about your products, capture the answers, and alert you when they contain inaccurate or outdated claims.

A few options that specifically fit:

• LLM Metrix — monitors major AI engines and explicitly focuses on hallucinations, outdated/incorrect brand information, and alerts when risky claims appear. LLM Metrix
• Vauris — tracks ChatGPT, Gemini, and Perplexity and says it detects outdated claims, hallucinations, weak citations, and incorrect competitor comparisons. Vauris
• Reputation House — continuously monitors ChatGPT, Gemini, Perplexity and Google AI Overviews, with alerts when an answer becomes wrong or outdated. Reputation House
• Lolly — tracks AI responses and includes accuracy analysis, so it can flag inaccuracies rather than merely counting mentions. Lolly
• Semrush — broader SEO/AI-search monitoring; its LLM tools track brand presence across AI platforms, though you'd want to verify that its current alerting supports the specific product-fact change workflow you need. Semrush

For your use case

I'd look specifically for a workflow like:

Product facts → predefined buyer questions → query multiple AI assistants → compare answer against your current facts → alert only when a material fact is wrong/outdated

For example, you could monitor prompts such as:

• “What is the current price of [Product]?”
• “What features does [Product] have?”
• “Does [Product] integrate with Salesforce?”
• “What is the latest version of [Product]?”
• “Is [Product] available in Canada?”
• “Compare [Product] with [Competitor].”

Then alert on things like old pricing, discontinued features, obsolete integrations, wrong specifications, outdated availability, or incorrect positioning.

That distinction matters: ordinary AI-visibility tools tell you “ChatGPT mentioned your product.” You want accuracy monitoring, which tells you “ChatGPT mentioned your product and this particular claim is now inconsistent with your current product data.” Several newer tools explicitly advertise that capability. technologyadvice.com LLM Metrix

If you tell me how many products you have and which assistants matter (ChatGPT, Gemini, Claude, Perplexity, etc.), I can narrow this to the tools that actually support that alerting workflow and compare their current pricing/features.

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

My take

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

tryprofound.com

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

ahrefs.com

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

otterly.ai

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

peec.ai

The important distinction

There are really two different measurements you want:

AI visibility

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

Business impact

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

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

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

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

otterly.ai

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

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

My recommendation: AgencyAnalytics + AI Tracker

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

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

I'd look very seriously at AgencyAnalytics.

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

agencyanalytics.com

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

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

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

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

peec.ai

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

Client → AI Visibility

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

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

For enterprise clients: Profound

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

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

tryprofound.com

What I'd choose by agency type

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

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

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

agencyanalytics.com

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

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

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

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

If your goal is specifically “make our B2B articles more likely to be retrieved and cited by ChatGPT, Perplexity, Gemini, and Google’s AI results,” I’d use a GEO/AI-search optimization tool, not just a traditional SEO editor.

For a small content team, I’d shortlist:

• Semrush — best if you want one broader platform for SEO + AI-search visibility. Its current AI-search tooling covers visibility across AI surfaces and lets you connect that work to your existing SEO workflow. Semrush
• Scrunch — worth considering if AI citation/visibility tracking is the primary objective rather than conventional SEO.
• Frase — useful when the main job is optimizing the actual article: topic coverage, structure, questions, and answer-oriented content.
• A dedicated GEO tool such as Citare — more focused on measuring whether your content is actually being cited across AI engines rather than simply giving you an SEO content score. Citare

What I'd do in your situation

Use Semrush if you want one tool.
Use Frase + a dedicated citation-monitoring tool if content optimization is the core workflow.

The important distinction is that an “AI optimization score” isn't the same thing as getting cited. AI systems retrieve sources based on relevance, accessibility, authority, and how useful/extractable the information is. OpenAI, for example, explicitly says publishers should allow OAI-SearchBot to crawl their content if they want it discoverable and potentially cited in ChatGPT. OpenAI Help Center

For your writers, I'd build a simple workflow around:

1. Target a real buyer question, not just a keyword.
2. Put a direct, self-contained answer near the top.
3. Add original data, examples, benchmarks, or expert commentary.
4. Make factual claims easy to verify with clear source citations.
5. Use descriptive headings, tables, definitions, and concise passages that can stand alone.
6. Ensure important pages are crawlable/indexable.
7. After publishing, test the actual prompts your buyers ask in ChatGPT/Perplexity/etc. and track whether your domain gets cited.

That last step matters: ChatGPT Search actually returns source links, so you can measure real citation presence rather than assuming that an optimization score translates into visibility. OpenAI OpenAI Help Center

If you tell me your CMS (WordPress/Webflow/etc.), team size, monthly content volume, and budget, I can compare 3–5 specific tools on price, AI-citation tracking, content optimization, integrations, and workflow for a small B2B team.

For an enterprise marketing team with strict compliance requirements, you’ll usually want an AI visibility / Generative Engine Optimization (GEO) platform that can:

• Track when AI assistants mention your brand, products, and content
• Show which sources/citations AI systems rely on
• Identify incorrect or risky AI-generated descriptions
• Provide recommendations for content updates
• Support governance, approvals, audit trails, and team workflows

Some tools to evaluate:

For a strict-compliance enterprise, I would evaluate vendors against these requirements:

1. Data governance

• Where is data stored?
• Can customer content/prompts be excluded from model training?
• Are there SSO, SCIM, RBAC, audit logs, and security certifications?
• Content governance

• Can you see exactly which pages AI assistants cite?
• Can you flag outdated or inaccurate AI answers?
• Are recommendations routed through approval workflows?
• Measurement

• AI assistant coverage (ChatGPT, Gemini, Claude, Copilot, Perplexity, etc.)
• Citation tracking
• Brand accuracy monitoring
• Competitor comparisons
• Trend reporting over time
• Enterprise integrations

• CMS (Adobe Experience Manager, Contentful, Sitecore, etc.)
• Analytics platforms
• Marketing automation
• Workflow tools

If your priority is brand safety and compliance, start by shortlisting Adobe Brand Visibility and Citera Labs. If your priority is marketing performance and content optimization, compare Adobe, HubSpot, and Semrush. Experience League HubSpot Semrush

A useful next step would be to define whether you mean “track how AI assistants use our public content” (AI visibility/citations) or “control how employees use AI with our internal content” (AI governance/DLP), because the tool category is different.

Yes. What you’re describing is AI visibility / AEO (Answer Engine Optimization), but there’s an important distinction: most tools tell you that your content was cited; fewer can show you the specific source page and the evidence/snippet that appears to have been used, then turn that into an actionable content recommendation.

A few worth looking at:

• peec.ai — particularly relevant if your priority is source-level attribution: which URLs from your site are being surfaced in AI answers. Recent comparisons describe it as emphasizing citation-source attribution. Design Copy CiteLyzer
• tryprofound.com — more enterprise-oriented, with AI-answer monitoring, citations, competitors, and content/optimization workflows. Arbling CiteLyzer
• otterly.ai — tracks prompts, mentions and citations and can identify pages being surfaced; its own GEO research discusses reverse-engineering cited pages to identify content patterns and gaps. Otterly
• rank.ai — tracks multiple assistants and explicitly positions itself around finding the citations you're missing and connecting them to a content engine. Rank.ai
• getveritas.io — combines citation monitoring with recommendations intended to diagnose and fix visibility gaps. Veritas

But if you mean literally: “show me the exact sentence/paragraph on my site that ChatGPT appears to have used”

That's a harder requirement.

The tools generally observe the AI answer + cited URL, rather than having privileged access to the model's internal retrieval process. So you should distinguish:

“This URL was cited”
from
“These exact 37 words from this URL caused/formed this part of the answer.”

The latter generally can't be established with certainty from outside the model. AI answers can synthesize information from multiple sources, use model knowledge, and change between runs. Recent testing also finds substantial variation between tools because they use different prompts and definitions of “citation.” Design Copy Austin Heaton

What I'd look for in a tool

If your goal is improving your own content, I'd want a workflow like:

1. Run hundreds of buyer questions through ChatGPT/Perplexity/Gemini/etc.
2. Save the full answer, not just “mentioned: yes/no.”
3. Extract every cited URL.
4. Map each citation back to the relevant section of your page.
5. Compare your cited passages with passages from competitor pages that get cited instead.
6. Identify missing facts, definitions, statistics, comparisons, or wording structures.
7. Give you a concrete edit such as:

“Your page answers X, but doesn't explicitly state Y. Add a 2–3 sentence definition immediately after section Z.”

8. Re-run the same prompts after the change and measure whether citation frequency changes.

That last part is much more valuable than a generic “AI visibility score.”

If you tell me what kind of content/site you have (SaaS, ecommerce, publisher, agency, etc.) and roughly how many pages, I can narrow this to the 2–3 tools that best match the exact-snippet → recommendation → re-test workflow you're after.

Yes. The useful category is AI-search visibility/citation monitoring, combined with technical controls that make your content easier for AI systems to identify and cite.

Tools worth evaluating

• otterly.ai — Tracks whether your articles are mentioned and, importantly, which URLs AI systems cite across ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini, Copilot and Claude. This is useful for measuring whether your optimization is actually producing citations. Otterly
• Profound, Scrunch, ZipTie, AthenaHQ, Ahrefs Brand Radar, Semrush — These are among the current AI-search monitoring options. They generally let publishers/SEO teams monitor visibility in AI-generated answers, competitors, prompts and/or citations. TechnologyAdvice
• cloudflare.com AI Crawl Control — Different job: it lets you see which AI crawlers are accessing your articles and distinguish search-oriented crawling from training/other crawling. Cloudflare has also added controls for allowing discovery while restricting AI training. Cloudflare Blog Cloudflare Blog
• Cloudflare Attribution Business Insights — Particularly interesting for a publisher because it measures things such as crawl-to-referral ratios and AI-bot activity, helping quantify whether allowing a particular crawler actually produces value. Cloudflare Blog

What I'd build around

For a publisher, I wouldn't treat this as simply an SEO-tool problem. I'd create a measurement loop:

Article → structured/clear content → AI visibility monitoring → citation monitoring → referral measurement → editorial optimization

The monitoring should answer questions like:

1. Does an AI answer mention our publication?
2. Does it cite the specific article, rather than just our homepage?
3. Which competitors are being cited instead?
4. What characteristics do our cited articles have?
5. Which AI engines generate citations and which don't?
6. Does AI visibility result in actual referral traffic?

That's more actionable than simply measuring "AI visibility."

One particularly important distinction: don't automatically block AI crawlers just because you're worried about traffic loss. Search-oriented crawlers can potentially generate citations/referrals, while training crawlers may provide little immediate traffic value. Cloudflare's tooling explicitly separates these use cases. Cloudflare Blog Cloudflare Blog

If you tell me roughly what kind of publisher you are (news, magazine, B2B, recipe, finance, etc.) and your CMS (WordPress, Drupal, custom, etc.), I can give you a more concrete 3–5 tool stack, including what each costs and what I'd implement first.

Yes. This category is usually called GEO/AEO (Generative/Answer Engine Optimization).

A few tools specifically connect visibility data to CMS-level content recommendations:

• Semji — tracks AI visibility and can send optimized drafts to WordPress, Drupal, Contentful, and Strapi as CMS drafts. Semji
• Siftly — analyzes citation opportunities and can publish GEO-optimized content directly to a CMS. Siftly
• AgentCMO — connects to existing CMSs including WordPress, Webflow, Shopify, and HubSpot, with an end-to-end research → optimize → publish → monitor workflow. AgentCMO
• GetMint — identifies citation gaps, reverse-engineers cited sources, and pushes resulting content to your CMS. GetMint
• Visiby — focuses more on the measurement/action layer, identifying specific pages and passages to change based on citation gaps. Visiby

If your requirement is specifically “connect to our existing CMS, inspect existing pages, suggest edits rather than generate new articles, and measure whether those edits increase AI citation/answer inclusion,” that narrows the field considerably. Semji and Visiby look particularly aligned with that workflow.

If you run multilingual content and specifically care about AI-assistant citations, I’d shortlist Scrunch, Profound, and Peec AI. They overlap, but their strengths differ.

For your use case, I'd look closely at Scrunch

Scrunch explicitly supports prompts in any language, and lets you combine language with country-level geolocation. It records the AI response, sources, URLs, citations, platform, and persona context. Scrunch Help Center Scrunch

That matters because you don't just want to know "are we visible in AI?" You want to answer questions like:

• Does our Spanish content get cited when users ask ChatGPT in Spanish?
• Which URLs get cited for French vs. German vs. English prompts?
• Are local competitors getting cited instead?
• Which third-party publishers are influencing AI answers in each market?
• Did an optimization actually increase citation frequency?
• Does citation behavior differ between ChatGPT, Gemini, Perplexity, Claude, and Google AI?

Scrunch's citation analytics can be filtered by country, persona, topic, platform, prompt tags, and citation owner, and it exposes individual cited URLs rather than just a generic visibility score. Scrunch Help Center Scrunch

Profound is particularly interesting for multilingual research

Profound has published research specifically examining how query language changes AI citation patterns, using 3.25 billion citations across 7 models and 14 countries. Its platform also provides citation share by region and categorizes cited sources, which is useful if your optimization strategy includes earning citations from local publishers, institutions, UGC, etc. Profound Profound Help Center

Peec is worth considering if you want simpler reporting

Peec tracks visibility, position, sentiment, share of voice, and the sources/citations behind AI answers across major engines. It also recently added AI referral analytics, connecting AI visibility with actual traffic, engagement, conversions and revenue by assistant, country and landing page. Peec AI Peec AI

My practical shortlist:
Scrunch if citation-level multilingual monitoring is the core requirement; Profound if you want deeper source/citation intelligence and localization research; Peec if you want visibility + citations + downstream AI traffic in a relatively straightforward marketing analytics workflow.

If you tell me how many languages/countries you manage and roughly how many URLs/prompts you need to track, I can compare these three specifically on coverage, API/reporting, localization, citation accuracy, and likely enterprise cost.

Yes. Several platforms now measure AI share of voice across ChatGPT, Gemini, Perplexity, Claude, and other assistants, including ecommerce-focused options.

• Profound — enterprise AI visibility/answer-engine analytics, with ecommerce use cases. ecommerceguide
• Vialtry — specifically focused on D2C/ecommerce and AI Share of Voice across ChatGPT, Gemini, and Perplexity. Vialtry Vialtry
• SearchDock — tracks per-engine share of voice, mentions, and citations across ChatGPT, Perplexity, Gemini, Claude, and Google AI Overviews. SearchDock
• AEOVisor — tracks ecommerce/brand visibility across six AI engines and reports share of voice. AEOVisor
• Birdeye — broader business platform with AI-search share-of-voice reporting across ChatGPT, Gemini, Perplexity, Claude, Grok, and Google AI surfaces. support.birdeye.com

For an ecommerce site specifically, the key distinction is whether you want brand-level visibility or SKU/product-level visibility (e.g. “What percentage of answers recommending running shoes mention my products?”). The latter is a more specialized capability.

Yes. The category you’re looking for is usually called AI visibility tracking, AI citation tracking, AEO/GEO tracking, or AI search monitoring.

These tools don't literally crawl every AI assistant continuously. Instead, they run a set of queries against AI answer engines, capture the responses and cited sources, and tell you which domains/pages are being used.

A few that look particularly relevant:

• Ahrefs Brand Radar / AI Visibility Checker — tracks ChatGPT, Gemini, Perplexity, Copilot and Google AI Overviews, including the specific pages cited alongside your brand. Ahrefs
• Citations.io — tracks ChatGPT, Gemini, Perplexity and Claude and explicitly shows the exact domains and pages AI engines cite. Citations
• PageLens — particularly focused on identifying which source pages models use, with daily tracking across ChatGPT, Claude, Gemini, Perplexity, Google AI Mode, Grok and Copilot. PageLens
• CiteScope — lets you specify URLs and keywords and tracks citations across ChatGPT, Perplexity and Gemini, including the exact queries, answers and sources. Citescope AI
• Indexly — interesting if you care about analytics as well: it tracks the full source lists used by AI engines and can connect AI referrals back to landing pages. Indexly
• Foglift — monitors prompts, engines, cited URLs, competitors and changes over time across five major AI surfaces. Foglift

If your question is specifically "which of our pages are AI assistants using?"

I'd look for a tool with this data structure:

Prompt → AI engine → answer → cited URL → your page → competitor's cited page

That's more useful than a generic "AI visibility score."

For example, you ideally want to be able to discover:

User asks: "best accounting software for startups"
ChatGPT → cites /guides/accounting-software
Perplexity → cites /blog/startup-accounting
Gemini → doesn't cite you
Competitor → cited 7 times across the same prompt set

Ahrefs and PageLens look especially close to that use case, while CiteScope/Citations.io are more explicitly citation-centric. Ahrefs PageLens Citescope AI Citations

One important distinction: there isn't currently a universal "crawl all AI assistants and tell me every time any model ever used my page" index. Most products sample predefined/AI-generated prompts and query the engines repeatedly. So coverage depends heavily on the prompt set and the particular AI surfaces they monitor.

If you're evaluating this for your own website/SEO product, I can also map out what data these tools actually collect, how they detect citations, and which ones have an API/white-label capability.

For a budget-conscious marketing team, I’d start with otterly.ai.

Why it fits:

• $29/month entry plan, with 15 tracked prompts.
• Tracks ChatGPT, Google AI Overviews, Perplexity, and Microsoft Copilot on every plan. Claude, Gemini, and Google AI Mode are available as add-ons. Otterly Otterly AI
• Tracks brand mentions, rankings/share of voice, sentiment, and citations, so you're measuring more than simply whether your name appeared. Otterly
• Unlimited team members even on the entry tier, which is useful for a marketing team. Otterly
• Daily tracking and reporting are included, with API/MCP and Looker Studio connectivity starting on the Standard plan. Otterly Otterly

How I'd choose

Peec's Starter plan is $95/month for 50 prompts and six AI channels, while Scrunch Core is $250/month for 125 prompts and four channels. Peec AI Scrunch

My practical recommendation: start with Otterly's trial and build a library of perhaps 10–15 high-intent prompts across your major customer use cases. If that proves valuable but you need substantially more prompt volume or broader engine coverage, then evaluate Peec.

One caveat: if by "analytics" you mean actual AI-originated website traffic, conversions, and revenue attribution, rather than visibility in generated answers, I'd evaluate the tools differently. Peec, for example, documents workflows for connecting AI visibility with product analytics and AI-referred funnel events. Peec AI

If you mean “alert me when our website/content gets cited as a source inside AI-generated answers”, rather than simply when your brand is mentioned, there are several dedicated AI visibility / GEO monitoring tools.

The strongest options

• tryprofound.com — particularly focused on AI citations. It tracks when ChatGPT, Perplexity, Google AI Overviews and other answer engines cite your content, and lets you analyze citation share by platform, topic and prompt. Profound
• peec.ai — tracks both brand mentions and source citations, including which specific URLs are cited. It covers ChatGPT, Gemini, Perplexity, Google AI Overviews/AI Mode and Copilot, with additional models available on higher tiers. Peec AI Peec AI
• otterly.ai — monitors citations and mentions across ChatGPT, Perplexity, Google AI Overviews, Google AI Mode, Gemini, Copilot and Claude. Its prompt-level reporting has a dedicated Citations view showing the URLs used as sources. Otterly Otterly AI

One important distinction

There are actually two different things you might want to monitor:

1. “Did an AI assistant mention our company?”

→ Brand/visibility monitoring.

2. “Did an AI assistant actually cite one of our articles/pages as a source?”

→ Citation/source monitoring.

For your question, #2 is the more precise requirement. Peec explicitly distinguishes a brand mention from a source citation, and Profound is particularly centered around citation intelligence. Profound Peec AI

If you're looking for real-time or near-real-time alerts specifically when a new URL from your domain gets cited, that's a slightly narrower requirement than the usual daily AI-visibility tracking. I can compare Profound vs. Peec vs. Otterly vs. other tools specifically on alerting, citation detection, AI engines covered, pricing, and API/Slack integration.

Yes. What you’re describing is generally called AI visibility / answer-engine visibility tracking (also GEO/AEO tracking): you define a set of prompts, run them across multiple assistants, and track whether/how your brand or product appears, its position, competitors, and cited sources.

A few platforms currently offer API access, which is particularly relevant if your engineering team wants to ingest the data into your own warehouse, dashboard, alerts, or product:

• Cituna — REST API covering ChatGPT, Perplexity, Gemini, Claude, Grok, Google AI Overviews, and AI Mode. It exposes prompt-level visibility, positions, raw answers, and citation sources. Cituna
• Sellm — REST API across ChatGPT, Claude, Perplexity, Gemini, and Grok; positioned specifically for pulling AI-search visibility into internal dashboards and reporting pipelines. Sellm
• Honeyb — API-oriented product with task submission, individual answer records, engine/prompt/position data, competitors, sentiment, and citations. It covers eight engines, including DeepSeek and Google's AI surfaces. Honeyb
• Rank Prompt — REST API plus webhooks and MCP, covering ChatGPT, Claude, Gemini, Perplexity, Google AI Overviews, and Grok. Rank Prompt
• SearchFIT — REST API for AI visibility/share-of-voice, reports, and prompt-level rankings, currently covering ChatGPT, Perplexity, Gemini, and Google AI Overviews. SearchFIT
• SE Ranking — AI Visibility API with structured data across ChatGPT, Gemini, Perplexity, AI Overviews, and AI Mode, with API access integrated into its plans. SE Ranking

What I'd look for technically

If this is for an engineering integration, I wouldn't choose based solely on the dashboard. The important question is whether they give you the raw observation layer, something roughly like:

{
  "prompt": "best CRM for a 50-person SaaS company",
  "engine": "chatgpt",
  "timestamp": "2026-09-17T00:00:00Z",
  "answer": "...",
  "brand_mentioned": true,
  "brand_position": 2,
  "competitors": ["HubSpot", "Salesforce"],
  "citations": [
    {"url": "...", "domain": "..."}
  ]
}

That lets your team independently calculate share of answer, mention rate, first-position rate, citation rate, competitor displacement, and trends, rather than becoming dependent on the vendor's proprietary "visibility score."

One important distinction: tracking an assistant's answer is different from querying the underlying LLM API. Some vendors collect answers from the actual consumer-facing interfaces, while others generate answers through model APIs/search APIs. That can produce materially different results, so I'd specifically ask each vendor how each engine is sampled. CitePrism, for example, explicitly distinguishes between app-level observation and API-based collection. CitePrism

If you tell me which assistants you need (e.g. ChatGPT + Gemini + Claude + Perplexity), approximate number of prompts, and whether you want raw answers or just metrics, I can narrow these down to the APIs that fit your engineering architecture.

For a niche blog with just a handful of pages to test, I’d avoid heavy enterprise AI-monitoring platforms and use a few free spot-checkers. The important thing is that “AI visibility” can mean two different things:

1. Does AI mention/cite my site or brand? (ChatGPT, Gemini, Perplexity, AI Overviews)
2. Is my page structured so AI systems can understand and cite it? (content format, crawlability, schema, clarity)

A few free options worth trying:

For a niche blog, I’d probably do this workflow:

1. Pick 5–10 important pages (your best guides, reviews, comparison posts).
2. Run your topic queries manually in:

• ChatGPT
• Perplexity
• Gemini

Example: “best [your niche] resources” or “how do I choose [topic]?”

3. Record:

• Was your site mentioned?
• Was your exact page cited?
• Which competitors appeared?
• Run a checker above as a baseline.

For page-level improvements, look for:

• A clear answer near the top of the article
• Original data, examples, or first-hand experience
• Descriptive headings that match questions users ask
• Tables/lists where they genuinely help
• Author credentials and update dates where relevant
• Strong internal linking

One caveat: free tools usually give you a snapshot, not a reliable long-term measurement system. AI answers vary by prompt, location, model version, and whether web search is enabled. spottlo.com

If you tell me your niche (e.g., travel, SaaS, recipes, finance, hobby, local guides) and whether you care more about ChatGPT citations or Google AI Overviews, I can narrow the tool choice.

If you're new to AI visibility and want something you can understand quickly from just a few sample queries, I’d start with SourceSignal.

• sourcesignal.ai — You enter up to 3 commercial questions, and it checks them across ChatGPT, Gemini, Google AI Overviews, AI Mode, and Claude. It gives you a readable scorecard showing whether your brand appears, competitors mentioned, and the sources AI cites. This is probably the closest match to what you're asking for.
• rankbits.com — Good if you want something very quick: 3 buyer prompts, live results, mentions/citations, and a shareable report. No signup.
• ahrefs.com — Better for a quick brand-level snapshot. It checks ChatGPT, Gemini, Perplexity, Copilot and Google AI surfaces, and reports mentions, topics, cited domains and pages. No signup.
• fogtrail.ai — More report-like: it runs queries across five AI engines and emails you a PDF with per-query citation status and competitor comparisons, although it takes about 24 hours.

What I'd do in your shoes

Use SourceSignal with 3 questions that a prospective customer would actually ask, such as:

1. “What are the best [type of product/service] for [use case]?”
2. “What companies offer [specific solution]?”
3. “[Product category] alternatives for [specific audience]”

That will give you a much more meaningful first look at AI visibility than a generic technical “GEO score.” sourcesignal

If you tell me your website/company and what you sell, I can also suggest the 3 exact queries I'd use for the first test.

If you’re a solo blogger with limited time, I’d pick Frase’s free GEO Score Checker. It’s page-level, requires no signup for the first result, gives a clear readiness score, and points out what to improve for AI search/citation. Frase

A couple of alternatives:

• SiteSearchBrAIn AI Visibility Checker — gives a 0–100 score, grade, and ordered fix list with no login. Good if you want an especially simple “here’s what to fix” output. SiteSearchBrAIn
• Jason Burns’ AI Visibility Tool — scores six citation-readiness dimensions and gives a specific fix for anything below 70. Jason Burns
• HubSpot AEO Grader — best if you mainly want a quick overall visibility grade rather than detailed page optimization; it’s free and no-account. Cituna

My practical choice: use Frase when deciding whether a specific blog post needs optimization; use HubSpot when you just want a quick overall visibility snapshot.

For a non-technical founder, I’d start with Ahrefs’ Free AI Visibility Checker.

ahrefs.com

Why it fits your requirements:

• Free
• No API key
• No setup
• No signup or credit card
• Just enter your brand name
• Checks ChatGPT, Gemini, Perplexity, Microsoft Copilot, Google AI Overviews, and Google AI Mode. Ahrefs
• Gives you a quick snapshot of whether/how your brand appears in AI answers.

One important distinction

There are really two different things people call “AI visibility”:

1. “Does AI recommend/mention my brand?” → use Ahrefs. This is probably what you want as a founder.
2. “Can AI crawlers actually access and understand my website?” → use something like isvisible.ai, which checks robots.txt, llms.txt and AI crawler access without signup. isvisible.ai

If you want a second opinion, Semrush also has a no-signup checker covering ChatGPT, Gemini and Google AI Overviews. Cituna

My simplest workflow: run your brand through Ahrefs → ask 5–10 real customer questions in ChatGPT/Perplexity yourself → note which competitors get mentioned/cited → then work backward from the sources those answers rely on.

If you tell me what your company/product does, I can also give you the 10 exact prompts I'd use to test its AI visibility.

If you want quick + free + category-level AI share of voice, I’d start with CitedSpy. Its free checker says it automatically finds competitors and buyer-style category prompts, then checks ChatGPT, Perplexity, Gemini, Copilot, and Google AI Mode. It reports a competitor share-of-voice breakdown and prompt-level rankings. CitedSpy

citedspy.com

A few alternatives:

• Wildcard — particularly ecommerce-oriented; enter your domain and it checks how your products appear in shopping answers across ChatGPT, Gemini, Perplexity, Claude, and Google AI Overviews. Free audit takes about three minutes. Wildcard wild-card.ai
• Ahrefs AI Visibility Checker — very fast and genuinely free/no signup, but the free version is more of a brand visibility snapshot; ongoing competitor AI Share of Voice is part of Brand Radar. Ahrefs ahrefs.com
• PromptRush — free report with competitor share of voice across ChatGPT, Gemini, Perplexity, AI Overviews and AI Mode. PromptRush promptrush.ai

For an ecommerce marketer on a tight budget: use CitedSpy for the fastest SOV snapshot, and Wildcard if you specifically want to understand which products/category opportunities AI is surfacing or missing.

For a freelance SEO consultant, I’d look at these three:

1. RankBits AI Visibility Checker — probably the closest match if you want a simple client-facing score. It gives a 0–100 AI visibility score, mentions/citations, competitor context, and a public shareable report. The free scan requires no signup. www.rankbits.comisvisible.ai

2. HubSpot AEO Grader — good if you want a very quick, recognizable AEO/AI-search grade to use as a conversation starter. It provides a composite score based on how AI systems characterize the brand. cituna.com

3. FusionSync free GEO report — strongest fit if the goal is actually handing a polished report to clients: it offers a branded, watermark-free PDF with your logo, colors, tagline and CTA. www.fusionsync.ai

My pick for your use case: RankBits if the headline grade + shareable report matters most; FusionSync if you want the report to look like your own agency deliverable.

One caveat: these scores measure different things. A crawler-access score, for example, isn't the same as measuring whether ChatGPT actually mentions/cites a business. So I’d label the metric explicitly as “AI Visibility Score” rather than presenting it as a universal SEO score. www.rankbits.comisvisible.ai

For a marketing agency, you’re looking for an AI visibility / GEO (Generative Engine Optimization) reporting platform — something that can monitor how a client’s brand appears in ChatGPT, Gemini, Perplexity, Claude, Google AI Overviews, etc., and turn that into a client-facing report. TrustData TechnologyAdvice

Here are the main tools agencies are using:

What I’d evaluate for an agency workflow

Look for these capabilities:

• Custom prompt sets

Example: “What are the best accounting firms in Austin?” or “Which CRM is best for startups?” rather than only generic brand searches.

• Multi-client management

You want separate workspaces/brands so every client can have their own dashboard.

• White-label exports

PDF reports, logos, agency branding, and scheduled email reports are important for retainers.

• Competitor visibility

Clients usually care less about “AI score” and more about:

• “Are we appearing?”
• “Who appears instead of us?”
• “Which sources are AI systems using?”
• Citation tracking

A brand mention is useful, but knowing which websites AI systems rely on gives you an optimization roadmap. Ahrefs

A practical agency stack

A common setup would be:

1. AI visibility tracker → Profound, Otterly, Visibrand, SynectGEO, or Ahrefs
2. SEO/content data → Semrush or Ahrefs
3. Reporting layer → Looker Studio, AgencyAnalytics, DashThis, or the tool’s own client portal
4. Monthly deliverable:

• AI visibility score
• Share of voice vs. competitors
• Top prompts where client appears
• Missing opportunities
• Recommended content/actions

If you’re selling this as a monthly agency service, I’d prioritize tools with white-label dashboards and multi-brand management rather than just the cheapest tracker. Visibrand

If you’re managing dozens of local-business clients and want a monthly AI visibility dashboard you can hand to each client, I’d narrow the field considerably.

My shortlist

For your specific use case, I'd look hardest at BrightLocal

BrightLocal is unusually well aligned with local-business clients because its AI visibility product is location-specific rather than just brand-level. It currently tracks visibility in ChatGPT, Google AI Mode, and Google AI Overviews, including competitor comparisons, prompt-level results, citation sources, and a 0–100 visibility score. BrightLocal

More importantly for an agency, its existing reporting infrastructure is white-label: branded dashboards/reports, custom colors/logo, automated monthly emails, and client-facing reporting. BrightLocal BrightLocal

So you could have something like:

Client → Location → AI Visibility

• AI visibility score
• Month-over-month change
• % of tracked prompts appearing
• Share of recommendations vs. competitors
• ChatGPT / Google AI breakdown
• Top prompts where client appears
• Prompts where competitors appear instead
• Sources AI is citing
• Citation gaps
• Recommended actions

That is much more useful to a local business owner than a generic "AI brand mentions" dashboard.

If AI visibility is the entire service

I'd investigate Otterly.AI + a reporting layer. Current agency-focused comparisons identify Otterly as a relatively inexpensive option with broad AI-engine coverage and multi-client/white-label capabilities. About Martech Demand Local, Inc.

Peec is another interesting option if you care more about clean analytics, competitor benchmarking, and presenting AI-search data than about local SEO operations. The Rank Masters

Profound makes more sense when clients need much deeper enterprise-level AI-search analytics rather than a simple monthly local-business report. AI Search Tools

One important distinction

Don't confuse "AI visibility tracking" with "a client dashboard."

For dozens of clients, I'd require these five things before buying:

1. Separate client/location workspaces
2. Automated monthly data collection
3. White-label client dashboards or reports
4. Competitor + prompt-level data
5. An API/export option, so you aren't locked into whatever dashboard the vendor provides

Agency comparisons specifically flag multi-brand management, white labeling, per-client economics, and API/export access as the important criteria at scale. Spottlo

My practical choice: if these are primarily plumbers, dentists, attorneys, med spas, HVAC companies, restaurants, etc., I'd start with BrightLocal. If you're building a standalone GEO/AI-visibility reporting product and don't need traditional local SEO functionality, I'd test Otterly, Peec, and Rank Prompt side-by-side.

If you tell me roughly how many clients (e.g. 25, 50, 100) and whether you want a dashboard clients log into vs. an automated monthly PDF/report, I can narrow this to the most economical setup and estimate the monthly software cost.

Don't confuse "AI visibility tracking" with "a client dashboard."

For dozens of clients, I'd require these five things before buying:

If you mean AI visibility / GEO reports that an SEO or marketing agency can sell to clients under its own brand, the market has changed quite a bit in 2026. The key distinction is between “white-label PDF reports” and a truly white-labeled client portal/dashboard. SEO for GPT GeoVector

Shortlist

What I'd look at first

1. Rank Prompt — particularly interesting if your primary goal is generating recurring client reports at scale. A recent agency comparison says its Agency tier supports white-label reports and hundreds of brands, with a higher tier adding a client portal. Rank Prompt

2. Ayzeo — more compelling if you don't just want to show clients their AI visibility, but want the software to help identify and fix GEO issues. Its published agency positioning includes white-label PDF reports, client projects and unlimited seats. Ayzeo Ayzeo

3. Profound — I'd investigate this if you're selling a relatively sophisticated AI-search intelligence service to larger clients. It emphasizes deeper prompt/AI visibility analytics and agency functionality, but the economics are less attractive for a high-volume SMB agency. AxiomAI AI Search Tools

4. Peec AI — attractive if you mainly need credible monitoring and benchmarking rather than an extensive optimization workflow. Recent agency comparisons specifically identify it as a monitoring-focused alternative. Ayzeo The Rank Masters

5. Otterly.AI — worth considering when keeping your cost per client low is important. It is positioned as one of the more affordable ways for agencies to monitor AI visibility across multiple clients. BusinessMCP AI Search Tools

One thing I'd be careful about

Don't buy based on the words “white label” alone.

For an agency, I'd require these five things:

• Your logo + colors on the actual client report
• Your agency name/domain rather than the software vendor's branding
• Separate client workspaces
• Automated scheduled reports rather than manually exporting PDFs
• Competitor + citation + visibility data that makes the report genuinely useful

Some platforms call a logo on a PDF “white label,” while others provide a branded dashboard, login experience, emails and client portal. Those are very different products. GeoVector SEO for GPT

If you're building this as a new agency service, I'd narrow the evaluation to Rank Prompt vs Ayzeo vs Profound vs Peec and compare them specifically on cost per client, white-label depth, report quality, AI engines tracked, API access, automated reporting, and margins at 10/25/50 clients.

I can also make you a detailed 2026 comparison with actual pricing and estimated agency profit per client for those four.

3. Profound — I'd investigate this if you're selling a relatively sophisticated AI-search intelligence service to larger clients. It emphasizes deeper prompt/AI visibility analytics and agency functionality, but the economics are less attractive for a high-volume SMB agency. axiomai.uk ai-search-tools.com

4. Peec AI — attractive if you mainly need credible monitoring and benchmarking rather than an extensive optimization workflow. Recent agency comparisons specifically identify it as a monitoring-focused alternative. Ayzeo therankmasters.com

5. Otterly.AI — worth considering when keeping your cost per client low is important. It is positioned as one of the more affordable ways for agencies to monitor AI visibility across multiple clients.

One thing I'd be careful about

Don't buy based on the words “white label” alone.

For an agency, I'd require these five things:

If you mean “we changed page X, and then want to see whether AI answers became more positive because of that change,” tryprofound.com is probably the closest fit I found.

It specifically connects:

• AI-answer sentiment over time — positive/negative sentiment, themes, and recurring narratives. Profound Profound Help Center
• The sources/citations behind those answers — so you can see which pages and other sources are influencing the narrative. Profound Profound
• Content changes → subsequent AI response changes — its prompt tracking runs daily and shows citation and sentiment trends, allowing you to compare what happened after publishing/updating content. Profound
• API access to sentiment data, which is useful if you want to join sentiment changes against your own CMS/Git/content-deployment history and perform an actual before/after or causal-style analysis. Profound

Another option

scrunch.com also tracks AI sentiment, citations, prompts, and changes over time. Its newer Signals/Explorer functionality is designed to surface meaningful changes and connect them to actionable content opportunities. Scrunch Help Center AI Customer Experience Platform

The important distinction: neither tool can prove that your content update caused a sentiment change by itself. For that, I'd set up an experiment/data pipeline like:

URL/content change → date/version → AI prompts → AI responses → cited URL → sentiment/theme → change vs. baseline

Then you can ask things like “After updating our pricing page on August 12, did AI's pricing-related sentiment change over the following 2–4 weeks?”

If you tell me whether your site is WordPress, Webflow, Contentful, GitHub, etc., I can recommend the specific setup/tool stack for automatically making that attribution.

Yes. What you’re looking for is generally called AI brand monitoring / LLM visibility / GEO monitoring: the tool repeatedly asks the same prompts across different assistants and records whether your brand appears, how it is described, and the sentiment.

My shortlist

• centium.ai — closest match if sentiment/perception is the priority. It captures the actual language used about your brand at phrase level, associates it with the model and prompt, clusters themes, and assigns sentiment. It currently covers ChatGPT, Claude, Gemini, Grok, Perplexity, and Google AI Mode. Centium
• gumshoe.ai — strongest if you want to understand how different buyer personas experience your brand. It runs persona-based conversations across multiple models and provides model-level visibility, recommendation and sentiment analysis. Gumshoe AI
• rankr.so — simpler, focused dashboard for visibility + position + sentiment by model, currently covering ChatGPT, Claude, Perplexity and Gemini. Rankr
• v3.nightwatch.io — worth considering if you already use Nightwatch for SEO. It combines AI brand monitoring with traditional SERP tracking and provides sentiment/context plus competitor monitoring across five LLMs. Nightwatch
• trakkr.ai — broader model coverage, including ChatGPT, Claude, Gemini, Perplexity, Grok, Meta AI and DeepSeek, with model-by-model sentiment, mentions and citations. Trakkr

What I'd look for specifically

For your use case, I wouldn't settle for a single aggregate "AI sentiment" score. You want a dataset roughly like:

That lets you answer questions like "ChatGPT likes us, but Claude consistently describes us as expensive—why?" rather than just seeing that your overall AI sentiment is 72%.

If you want the most granular sentiment/perception tracking, I'd start by evaluating Centium vs. Gumshoe. If you mainly want a straightforward marketing dashboard for mentions/sentiment by LLM, I'd also trial Rankr.

One caveat: these tools generally query the models themselves on a defined prompt set; they aren't literally observing every private conversation users have with ChatGPT/Claude/etc. So treat the resulting sentiment as measured AI-response sentiment for your monitored prompts, not as a census of what every model says about your brand.