Answers that name a competitor

For a small ecommerce shop on a tight budget, I’d start with a lightweight AI visibility tracker, not an enterprise platform. You mainly want to answer:

• “When shoppers ask AI assistants for products like mine, do I appear?”
• “Which prompts show competitors instead of me?”
• “Which product pages are being cited or ignored?”

A practical shortlist:

My recommendation for a small shop:

1. Start with Otterly.AI (or a similar budget tracker) for 1–3 months to establish a baseline. Track prompts like:

• “best [product category] under $X”
• “best alternatives to [competitor]”
• “where to buy [product type]”
• “[category] for [specific use case]”
• Track your top products, not every SKU. A small store usually gets more value monitoring:

• your best sellers
• highest-margin products
• products where competitors are strong
• Pair it with free checks:

• Ask ChatGPT, Gemini, and Perplexity the same buying questions every week.
• Log whether your product appears, whether your site is cited, and which competitors show up.

I would not start with enterprise tools like Profound unless you have a large catalog, a marketing team, or a need for executive reporting. LLM Visibility Tools

If you tell me your platform (Shopify, WooCommerce, Etsy, Amazon, custom site) and roughly how many products you have, I can narrow this down further.

For a marketing agency managing many client brands, I’d shortlist Peec AI and OtterlyAI, with Peec AI looking particularly well-suited to an agency workflow.

My recommendation: Peec AI

Peec AI is explicitly built around agency/multi-client use. Its agency setup gives you separate client workspaces under one account, centralized billing, flexible prompt allocation, and client-facing reporting. Peec AI Peec AI

It tracks Share of Voice as well as visibility, mentions, position, citations and sentiment across ChatGPT, Perplexity, Google AI Overviews, Google AI Mode, Gemini and Microsoft Copilot. Peec AI Peec AI

For an agency, the particularly useful pieces are:

• Separate workspace per client — keeps brands, competitors and prompts isolated.
• One account for your whole agency — rather than maintaining separate subscriptions/logins.
• Flexible prompt allocation — shift monitoring capacity toward larger or more important accounts.
• Automated reporting — including branded client dashboards and exports/API access.
• Multi-channel monitoring — useful if your definition of "AI share of voice" spans both chat assistants and Google's AI search experiences.
• Agency automation via MCP — Peec says you can automate weekly rollups across clients and generate summaries for Slack/Google Slides. Peec AI

peec.ai

Strong alternative: OtterlyAI

OtterlyAI is also compelling if client separation and scale are your primary concern. Its Workspaces are specifically designed for agencies managing dozens or hundreds of brands, with separate team access, reports and data for each client. Otterly Otterly.ai

It monitors the same six major AI/search surfaces and offers competitor comparisons, citation analysis, sentiment, exports and a Looker Studio connector. Otterly Otterly

otterly.ai

How I'd choose

Bottom line: I'd start with Peec AI for a marketing agency, particularly if you're selling ongoing AI-search/GEO retainers and need to turn SOV data into recurring client reports. I'd put OtterlyAI through the same trial if you have a very large client roster and want rigid workspace separation.

One thing I'd verify in demos: how they define and calculate SOV, because "visibility" and "share of voice" aren't interchangeable. Peec explicitly defines SOV as a brand's proportion of all tracked-brand mentions, rather than simply the percentage of answers where the brand appears. Peec AI

If you tell me roughly how many clients/brands you manage (e.g. 10, 50, 200+) and how many prompts per client, I can compare the likely economics and reporting setup of Peec vs. OtterlyAI for your agency.

For what you described—capturing AI citations and attributing them to the correct physical location for local-business clients—I’d look first at BrightLocal’s Local AI Visibility.

It is unusually well aligned with the workflow you’re describing:

• Location-level tracking: each location gets its own AI visibility data rather than treating the client as one national brand. BrightLocal BrightLocal
• Citation/source capture: it records the websites/domains that AI platforms cite in their answers, including citation counts and which platforms cite them. BrightLocal Help Center
• AI platforms: currently ChatGPT, Google AI Overviews, and Google AI Mode. BrightLocal
• Prompt-level attribution: you can define prompts for each location and see mentions, share of voice, position, competitors, and sources associated with those prompts. BrightLocal Help Center
• Local competitor context: it compares a specific location against the local businesses appearing for the same searches, rather than simply measuring brand-level visibility. BrightLocal
• Citation-building workflow: source/citation gaps can be connected to BrightLocal's Citation Builder, which makes it useful if you want to turn the reporting into an SEO deliverable. BrightLocal

brightlocal.com

The important distinction

I'd separate your requirements into two things:

1. "Was this location mentioned/recommended?"
BrightLocal is quite good for this.

2. "What URLs did the AI actually cite, and can I attribute each citation to Location A vs Location B?"
This is the more interesting requirement, and I'd test the actual export/API/reporting behavior before standardizing on a platform.

That's because AI citation data is still considerably less standardized than traditional rank tracking. A recent BrightLocal study of 200,000+ localized AI searches found substantial variation when the same query was rerun, including variation from changing the searcher's location—even within the same city. BrightLocal

Other tools I'd investigate

• Local Falcon — particularly interesting if you want AI visibility combined with highly granular local-search/grid data. It says it tracks ChatGPT, Gemini, Perplexity, Google AI Mode and local results. Local Falcon
• Semrush — broader AI visibility coverage, including ChatGPT, Gemini, Google AI surfaces and Perplexity, but its workflow is more domain/brand-oriented than the location-first model you're describing. Semrush Semrush
• A custom collection layer — worth considering if you're building this into your own agency reporting product. You'd store something like:

client
  └── location
       ├── prompt
       ├── AI platform
       ├── run timestamp
       ├── response
       ├── businesses mentioned
       └── citations
            ├── URL
            ├── domain
            ├── citation position
            └── attributed location

That last approach becomes especially attractive if you need API access, raw citations, historical snapshots, automated client reports, and multi-location attribution rather than simply a dashboard.

My practical recommendation: start by testing BrightLocal against 3–5 of your actual multi-location clients. If its raw citation data/export doesn't give you the granularity you need, I'd build a small custom collection/reporting layer rather than forcing a generic AI-visibility platform into a location-attribution workflow.

If you tell me how many locations you typically manage (e.g. 10, 100, 1,000+) and whether you need an API/white-label reporting, I can compare the current tools specifically for that agency use case.

If the requirement is specifically historical, enterprise-grade measurement of how your brand is represented across AI models, I’d shortlist Profound first, with Peec AI and Scrunch as comparison points.

Why I'd start with Profound

Profound is particularly aligned with your description: it is designed around systematically querying AI answer engines and measuring brand mentions, competitors, citations, prompts and changes over time, rather than simply doing a one-off audit. Current comparisons describe its enterprise offering as covering a particularly broad set of AI surfaces, including ChatGPT, Perplexity, Gemini, Google AI surfaces, Copilot, Claude and others. Bloomiro Geohats

For an enterprise procurement process, I'd specifically investigate:

• Historical time series: Can you retain raw observations indefinitely, rather than only displaying rolling aggregates?
• Model/version segmentation: Can you distinguish model changes from actual changes in brand visibility?
• Prompt-level history: Can you see exactly what the model said on a particular date?
• Citation/source history: Which publications, websites and pages caused the model to mention your brand?
• Competitor benchmarking: Can you compare your share of mentions against a defined competitive set?
• Geography/language: Can you separate US vs. UK vs. Germany, etc.?
• API/data export: Can you put the underlying observations into Snowflake/BigQuery rather than keeping the data trapped in the dashboard?
• Enterprise security: SSO/SAML, SOC 2, RBAC, auditability and contractual data-retention terms.
• Model-change handling: This is particularly important for historical analytics. A model upgrade can change answers even when your underlying brand presence hasn't changed.

Current industry comparisons identify Profound as the deepest enterprise-oriented option and report enterprise capabilities such as SSO/SAML, SOC 2 and API access. Those are vendor/comparison claims, so I'd verify them directly during procurement. Geohats

The important distinction

I'd avoid buying purely on the number of "AI models supported."

For your use case, the real asset is the historical dataset:

Prompt × date × model/surface × geography × brand response × competitors × citations × source pages

That lets your analytics team answer questions such as:

• "When did our visibility decline?"
• "Was the decline isolated to ChatGPT or also present in Gemini and Perplexity?"
• "Which competitors gained the mentions we lost?"
• "Which publishers/websites are repeatedly being cited instead of us?"
• "Did our visibility change after a particular product launch or PR event?"
• "Are model changes responsible for the apparent trend?"

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

My procurement approach

I'd run a 2–4 week bake-off with Profound, Peec and Scrunch using the same 100–500 prompts representing your actual customer journeys. Don't let vendors supply the benchmark prompts.

Require each vendor to provide:

1. Raw prompt/response history.
2. Daily or weekly observations.
3. Model/engine identification.
4. Citation-level data.
5. Competitor mentions.
6. Historical exports/API.
7. Data-retention policy.
8. Model-version/change methodology.
9. SSO/RBAC/security documentation.
10. A sample enterprise dashboard for marketing + executive reporting.

One particularly important test: ask each vendor to reproduce the same historical metric from raw data. If you can't reconstruct their headline "visibility" number yourself, you're buying a dashboard rather than an analytics dataset.

For a large enterprise, I'd therefore put Profound at the top of the initial RFP, while giving Peec AI and Scrunch a serious evaluation rather than assuming one vendor's marketing claims are definitive. Independent 2026 comparisons consistently place those three among the principal enterprise/mid-market options, although their assessments are not vendor-neutral. Bloomiro Toolsolved

If you tell me roughly company size, number of brands/products, countries, and whether you need API/raw-data access, I can narrow this to an enterprise shortlist and give you a vendor-by-vendor RFP scorecard.

I'd run a 2–4 week bake-off with Profound, Peec and Scrunch using the same 100–500 prompts representing your actual customer journeys. Don't let vendors supply the benchmark prompts.

Require each vendor to provide:

1. Raw prompt/response history.
2. Daily or weekly observations.
3. Model/engine identification.
4. Citation-level data.
5. Competitor mentions.
6. Historical exports/API.
7. Data-retention policy.
8. Model-version/change methodology.
9. SSO/RBAC/security documentation.
10. A sample enterprise dashboard for marketing + executive reporting.

One particularly important test: ask each vendor to reproduce the same historical metric from raw data. If you can't reconstruct their headline "visibility" number yourself, you're buying a dashboard rather than an analytics dataset.

For a large enterprise, I'd therefore put Profound at the top of the initial RFP, while giving Peec AI and Scrunch a serious evaluation rather than assuming one vendor's marketing claims are definitive. Independent 2026 comparisons consistently place those three among the principal enterprise/mid-market options, although their assessments are not vendor-neutral.

For thousands of ecommerce listings where you need visibility at the individual SKU/product-page level, I’d look specifically at SKU-level AI visibility platforms, rather than generic brand-level AEO trackers.

Tools worth evaluating

• ecommerceinsights.ai — explicitly resolves AI answers to individual SKUs, prompts, and engines. It says it can monitor catalogs across ChatGPT, Perplexity, Google AI Overviews, Gemini, Claude, and Copilot, with daily/weekly refresh depending on plan. eCommerceInsights.AI eCommerceInsights.AI
• zumihq.com — built around retail/ecommerce product visibility, tracking how AI describes, compares, and recommends products down to the SKU. It supports category-level rollups for larger catalogs. Zumi
• searchpanel.ai — tracks individual SKUs across multiple AI engines and provides a per-SKU visibility score, with daily monitoring. Searchpanel Searchpanel
• tryprofound.com — a broader enterprise AI-search platform that now has SKU-level shopping analysis, including related citations and keywords. Profound
• opttab.com — offers product/SKU visibility tracking alongside catalog, schema, feed, and AI-shopping readiness checks. Opttab

For your specific use case

If “I have 10,000+ listings and need to know which individual SKUs are being recommended, cited, or omitted by AI” is the core requirement, I'd shortlist eCommerce Insights, Zumi, and Searchpanel first. Their product-level positioning is substantially closer to the problem than tools that primarily measure brand mentions. Zumi eCommerceInsights.AI Searchpanel

The key capabilities I'd insist on are:

• SKU/variant-level tracking rather than just domain/brand visibility
• Bulk catalog ingestion/API rather than manually entering URLs
• Product-page ↔ AI-answer resolution
• Prompt/query segmentation by shopping intent
• Competitor and co-citation tracking
• Historical visibility by SKU
• Alerts when a high-value SKU disappears from answers
• API/CSV export so visibility can feed your ecommerce BI stack
• Coverage of the AI surfaces your customers actually use

One important distinction: monitoring whether ChatGPT mentions your brand isn't sufficient for a marketplace. You want something that can answer “Of these 20,000 products, which specific SKUs are appearing in buying answers, for which queries, in which engines, and how has that changed?”

If you tell me roughly how many SKUs (e.g. 10K, 100K, 1M) and whether you're a marketplace like Amazon/eBay or a first-party retailer, I can narrow this to the most scalable architecture and compare the API/bulk-monitoring capabilities.

For page-level AI citation → canonical URL attribution, I’d shortlist:

• otterly.ai — Tracks the specific URLs cited by ChatGPT, Perplexity, Google AI Overviews/Mode, Gemini, Copilot and Claude, with API access. Otterly Otterly
• tryprofound.com — Enterprise-oriented citation/AI-search analytics; worth evaluating if you need deeper reporting and larger-scale monitoring. Bloomiro
• scrunchai.com — Combines AI visibility with citation/content intelligence and API access. Toolsolved
• mentionsapi.com — More developer-oriented: explicitly resolves redirects, strips tracking parameters, deduplicates citations, and returns canonicalized URLs tied to prompts/providers. mentionsapi.com

For your specific requirement, I’d test Otterly and MentionsAPI first. The key evaluation criterion is whether they preserve the chain prompt → AI answer → cited source URL → resolved canonical URL → your page, rather than merely reporting that your domain was mentioned.

If you want AI Share of Voice trends alongside traditional SEO/search metrics in one dashboard, Semrush is probably the closest fit.

• Semrush — Its AI Visibility features track AI Share of Voice across ChatGPT, Google AI Overviews/AI Mode, Perplexity and Gemini, while its broader platform covers rankings, organic keywords, backlinks, traffic, and other SEO metrics. Semrush explicitly offers a unified view of AI visibility and SEO. Semrush Semrush
• Ahrefs Brand Radar — Also strong: it tracks AI Share of Voice, mentions, citations and impressions, with the impressions metric weighted using Google search-volume data. Ahrefs also has conventional Rank Tracker Share of Voice and SEO reporting. Ahrefs Help Center Ahrefs Help Center
• SERPrecon — More specialized around combining AI + Google SEO Share of Voice and trending it over time. SERPrecon

For a growth-marketing dashboard, I'd look at Semrush first if you want the widest combination of SEO, competitive research, AI visibility, reporting, and marketing data in one ecosystem. If your priority is specifically AI visibility + search-demand-weighted SOV, Ahrefs is worth comparing.

semrush.com
help.ahrefs.com

If your specific requirement is “track citation accuracy, detect incorrect attributions, and route each problem back to the responsible content team”, I’d separate the market into two categories.

Shortlist

• tryprofound.com — strongest fit if you want enterprise-grade AI-search monitoring. It tracks citations and the underlying sources across multiple AI engines. The important caveat is that the publicly documented model is primarily source/page attribution, not necessarily “this internal content team owns this error.” Plug LLM Visibility Tools
• clarity.microsoft.com — particularly interesting if you're already in the Microsoft ecosystem. Its Citation dashboard identifies cited pages, the queries associated with them, and AI referral traffic. Microsoft Learn
• hubspot.com — worth considering if your content operation already runs in HubSpot. It combines citation analysis with recommendations around content and outreach, which gets closer to turning detection into a workflow. HubSpot
• citations.io — interesting for the operational side of your use case. It exposes the prompt → answer → cited URL chain and produces implementation recommendations. Its own examples specifically include hallucination/misattribution alerts. Citations
• peec.ai / otterly.ai — good candidates if the primary requirement is monitoring AI visibility/citations rather than sophisticated internal issue routing. Plug

What I'd actually evaluate

For your use case, “citation count” isn't enough. I'd require the platform to preserve this chain:

Prompt → AI answer → claimed fact → cited URL → your canonical content → content owner/team → error type → ticket/status → corrected version → re-test

The critical distinction is between:

1. Citation presence — did the assistant cite your page?
2. Citation correctness — does the cited page actually support the claim?
3. Attribution correctness — did the assistant attribute the claim to the right organization/product/person?
4. Freshness — is the cited content current?
5. Ownership — which team owns the problematic source?
6. Remediation workflow — can the finding become a Jira/Linear/Asana/etc. task and be rechecked afterward?

Most AI-visibility products are much stronger at #1 than #2–#6. Microsoft's documentation, for example, explicitly describes citation counts and grounding queries but notes that citation activity itself doesn't establish ranking, importance, or causation. Search - Microsoft Bing Microsoft Learn

My recommendation for your architecture

If you're building this for a large content organization, I'd favor a monitoring platform with an API/export layer and put your own ownership model on top:

AI prompt library
       ↓
ChatGPT / Gemini / Claude / Perplexity
       ↓
Raw answer + model + timestamp
       ↓
Citation extraction
       ↓
Claim ↔ source verification
       ↓
Error classification
       ↓
URL → CMS/content owner mapping
       ↓
Content team queue
       ↓
Fix published
       ↓
Prompt re-run
       ↓
Accuracy / attribution trend

That gives you a much more useful KPI than “AI visibility”:

citation accuracy rate = supported citations / total citations

and then:

incorrect attribution rate by content team, content type, topic, model, and URL.

One especially important consideration: AI answers are nondeterministic and model/search systems change. A credible system should retain the exact prompt, engine/model, timestamp, answer and cited sources so an alleged attribution error can actually be audited. Independent methodologies in this space are increasingly emphasizing that reproducibility. Point Visible

If you tell me your stack (CMS + Jira/Linear/Asana + analytics + which AI assistants you care about), I can narrow this to 2–3 products and map exactly how I'd implement the “incorrect citation → responsible content team” workflow.

Yes — but there’s an important distinction.

If by “exact prompts” you mean the actual questions people are typing into ChatGPT/Perplexity/Gemini that result in your site being cited, there are now tools getting surprisingly close. But no tool can give you a complete, first-party log of every user's private ChatGPT prompt.

The tools I'd look at as a solo SEO freelancer

• tryprofound.com — probably the closest match to what you're describing. Its Prompt Volumes dataset is built from real, anonymized AI conversations, and it says you can search for a URL and see the prompts that cause AI engines to cite that page, including platform and volume data. Profound Profound Help Center
• otterly.ai — more geared toward an SEO freelancer who wants a practical monitoring/reporting platform. You give it prompts to track, and it records the exact prompt, AI response, mentions, and URLs cited. It also has AI Prompt Research to generate likely prompts from keywords, domains, or industries. Otterly AI Otterly AI
• peec.ai — another option for monitoring AI visibility. It tracks which prompts produce mentions and which sources are cited, with visibility/share-of-voice metrics. Peec AI

The distinction that matters

There are really three levels of data:

Profound is particularly interesting because it claims access to a large dataset of real AI conversations, rather than simply having an LLM invent likely prompts. Profound Profound

For a solo SEO freelancer, I'd think about it this way:

Profound = research into what people are actually asking AI

OtterlyAI = practical client-facing AI visibility monitoring

And there's a particularly useful workflow here:

Real prompts → prompts that cite competitors → pages being cited → why those pages are being cited → content recommendations for your client

That's much more actionable than simply reporting “your brand appeared in ChatGPT 23% of the time.”

One caveat: don't confuse the prompt that produced an AI answer with the search queries an AI model generated behind the scenes. Modern answer engines can fan out a user's prompt into multiple searches before constructing the answer. Profound, for example, explicitly tracks these “query fanouts.” Profound Help Center

If you're considering this specifically as a solo freelancer service, I can also break down which of these tools gives you the most useful data for selling an “AI SEO/GEO audit” to clients, including approximate pricing and what you could actually deliver in a $500–$2,000 audit.

For a midsize publisher, tryprofound.com is probably the closest match to that description.

It combines:

• AI crawler / agent analytics — shows which AI platforms crawl your site, how often, and which pages they access, plus AI-referred traffic. Profound
• Content-gap analysis — identifies high-volume prompts where competitors are being cited but your content isn't, so you can prioritize missing topics. Profound
• Answer-engine visibility — tracks mentions, citations, sentiment, and competitive visibility across major answer engines. Profound
• Content optimization — benchmarks pages against cited competitors and recommends structural, topical, schema, and FAQ improvements. Profound

A newer alternative worth evaluating is zerply.ai, particularly if you want to combine Search Console/GA4 data, AI citations, prompt gaps, and per-URL crawler logs in one system. Its site says it supports per-URL crawler logs and integrates Search Console, GA4, Bing, and AI-model data. Zerply

Short answer: If your defining requirements are “show me which AI crawlers are accessing our articles” + “tell our editorial team what content we're missing for AI answers,” I'd put Profound at the top of the initial shortlist. Profound

If you tell me roughly how many articles/domains you publish and whether you're on WordPress, Drupal, or a custom CMS, I can narrow this to 2–3 tools and compare pricing, crawler analytics, content-gap depth, and publisher workflow.

The category of solutions you’re looking for is usually called AI citation tracking, AI visibility tracking, Answer Engine Optimization (AEO) analytics, or Generative Engine Optimization (GEO) platforms. These tools track the prompts/questions that cause AI assistants to answer with your content as a cited source. Linkeddit Profound

Common solutions include:

The workflow typically looks like:

1. Seed the tool with your site, products, topics, or competitors.
2. The platform generates or lets you upload likely AI prompts, such as:

• “What are the best tools for X?”
• “How do I solve Y problem?”
• “Compare A vs B”
• “Who offers Z service?”
• It runs those questions through AI assistants.
• It reports:

• Which questions produced citations to your content
• Which pages were cited
• Which competitors were cited instead
• Which topics have citation gaps
• Which new content could improve your chances of being cited CitePrism Profound

If your goal is specifically “find the exact keywords/questions where AI assistants cite us today”, prioritize tools with prompt-level citation tracking rather than general AI brand monitoring. Linkeddit Profound

For an SEO/content team, I’d usually evaluate:

• Enterprise monitoring: Profound
• Prompt + citation intelligence: CitePrism or Peec AI
• Lower-cost experimentation: Frase or CiteRank

The important distinction: these tools generally measure test prompts they run, not the private questions real users type into ChatGPT or Gemini (those conversations are not available to third parties). CitePrism

If your agency’s core requirement is “which of our clients’ blog URLs are being cited or referenced inside ChatGPT, Perplexity, Gemini, Google AI Overviews/AI Mode, Claude, etc., and how does that change over time?”, I’d shortlist these:

My practical recommendation for an agency

I'd start with Otterly.AI and Peec AI.

Otterly looks particularly interesting if your agency needs to turn this into a repeatable client service: track a defined set of prompts, see the actual cited URLs, monitor competitors, and eventually feed the data into your own reporting/dashboard through an API. Otterly says its API exposes brand reports, prompts, citations, recommendations and GEO audits. Otterly

Peec is worth testing alongside it if your emphasis is more on marketing analytics and client reporting than SEO workflow. Current comparisons describe it as particularly suited to marketing teams and agencies. AnswerManiac Arbling

For a larger agency selling sophisticated GEO/AEO programs to enterprise clients, I'd put Profound into the evaluation. Its differentiator is depth rather than simply being a lightweight rank tracker. SEO Counselors

One important distinction

I'd specifically avoid buying a tool based primarily on a generic “AI visibility score.”

For your use case, you want the underlying evidence:

• Prompt: What question did you ask the AI engine?
• Engine: ChatGPT, Perplexity, Gemini, Claude, Google AI, etc.
• Answer: What did the engine actually say?
• Citation: Which URL(s) did it cite?
• Your URL: Was your actual blog article cited?
• Position: Where in the cited sources did your article appear?
• Competitors: Which competing URLs were cited instead?
• Frequency: How often is the URL cited across repeated runs?
• Historical change: Did citation frequency increase after you changed/published the article?
• Client reporting: Can you export/API the evidence rather than just a proprietary score?

That's important because being mentioned isn't the same thing as being cited. A brand can be named in an AI answer while a competitor's website supplies the actual source. Current industry comparisons explicitly distinguish mentions, citations, share of voice and prompt coverage. Austin Heaton

For a marketing agency, I'd structure the monitoring around URLs

Rather than asking only:

“Is Client X visible in ChatGPT?”

I'd want your system to answer:

“Of the 250 articles Client X has published, which URLs were cited by AI engines this month, for which buyer questions, by which engines, and how did that change versus last month?”

That lets you turn the data into an actual GEO content-retainer service:

Content published → AI prompts monitored → URLs cited → competing sources identified → content recommendations → changes implemented → citation lift measured.

If you tell me roughly how many clients, domains, and prompts you need to monitor per month (e.g. 20 clients × 100 prompts), I can narrow this down to the 2–3 platforms I'd actually demo, including current pricing and agency/multi-client limitations.

Yes. There’s now a fairly established category for this: AI visibility / LLM visibility / GEO (Generative Engine Optimization) monitoring.

These platforms generally run a fixed set of customer-style prompts against multiple AI assistants, save the actual answers, and detect:

• whether your brand was mentioned
• where in the answer it appeared
• whether your site was cited/linked
• which competitors were mentioned instead
• which third-party sources the model relied on
• how those metrics change over time

A few current options:

• ahrefs.com — tracks visibility across ChatGPT, Gemini, Perplexity, Copilot and Google AI Overviews; particularly interesting if you already use Ahrefs.
• searchprism.io — explicitly stores the full answers from ChatGPT, Claude, Gemini and Perplexity and counts brand mentions/citations over repeated runs.
• mentionscout.com — covers ChatGPT, Claude, Gemini, Perplexity, Grok, Google AI Overviews and AI Mode, with citation/source analysis.
• openlens.com — monitors seven AI platforms, including ChatGPT, Claude, Gemini, Perplexity, Grok and DeepSeek.
• rank.ai — focuses on recurring prompt checks and trends in whether models mention/cite your brand.
• citeprism.com — tracks mentions, recommendations, competitors and trusted sources across several AI assistants and Google AI surfaces.

The important distinction is that most of these aren't literally “crawling all AI assistant conversations.” They typically generate their own representative prompts and query the assistants on a schedule. That's necessary because private ChatGPT/Claude conversations aren't publicly crawlable. Some products also monitor publicly observable AI-search surfaces.

If what you're envisioning is “give me our brand + competitors, and continuously tell me every time an AI answer mentions us, exactly what it said, what source it quoted, and which model said it”, then yes—that product category exists, and several tools are already quite close to that exact workflow. searchprism.io mentionscout.com citeprism.com

If you're asking because you're considering building something in this space, there are some interesting gaps between these products that would be worth unpacking.

Yes. The category is usually called AI visibility / AI search monitoring. These tools repeatedly run a fixed set of prompts, save/analyze the answers, and show how your brand’s mentions, citations, positioning, and competitors change over time.

• OtterlyAI — tracks prompts across ChatGPT, Perplexity, Gemini, Claude, Copilot, and Google AI surfaces, with historical trends and answer-level analysis. otterly.ai Otterly
• Peec AI — another monitoring-focused option with recurring multi-model tracking and historical visibility data. RecoProof
• Profound — more enterprise-oriented, with deeper analytics and reporting. Bloomiro
• Ahrefs Brand Radar — combines AI visibility monitoring with traditional search/brand intelligence. AskedAbout

For your specific use case, I'd look for raw answer archiving, not just “share of voice.” You want to be able to say:

“On January 15, ChatGPT described us as X. On March 15 it changed to Y. The sources it cited also changed from A to B.”

That historical answer corpus can be considerably more valuable than a single visibility score. alignify.co

Yes. What you’re looking for is generally called AI visibility / GEO (Generative Engine Optimization) tracking: software that runs a set of real-world prompts against AI assistants and records whether your company/content is mentioned, recommended, and cited as a source.

A few options worth looking at:

• tryprofound.com — enterprise-oriented AI visibility platform with broad engine coverage and source/citation analytics.
• peec.ai — tracks prompts across AI search engines and analyzes brand visibility, competitors, and sources.
• otterly.ai — focused on monitoring AI search visibility and tracking prompts over time.
• promptwatch.com — particularly relevant if you want to run prompts repeatedly and measure changes after publishing/updating content.
• citations.io — explicitly tracks whether AI assistants mention your brand and which pages they cite. Its documentation says it samples ChatGPT, Gemini, Perplexity, and Claude and stores the underlying answers and detected citations. Citations Citations
• citeprism.com — tracks mentions, recommendations, competitors, and the sources AI systems use across ChatGPT, Claude, Gemini, Perplexity, Google AI Overviews/AI Mode, and Copilot. CitePrism

For your specific use case

I'd structure the test around citation attribution, rather than simply asking "does AI mention us?"

For example, create 50–200 prompts such as:

• "What are the best [category] tools for [use case]?"
• "How do I solve [problem]?"
• "What companies provide [service]?"
• "Compare [competitor] vs [your company]."
• "What are the most authoritative resources for [topic]?"
• "[Specific technical question your content answers]"

Then record:

The last two are particularly important because AI answers are nondeterministic. For example, Citations.io says it repeatedly samples the same prompts precisely because asking the same question can produce different brands/sources in different runs. Citations

If you tell me your website/content type and roughly how many prompts you want to test, I can narrow this to the 2–3 tools that fit and explain which one will actually show you the exact citations/URLs rather than just giving you an AI visibility score.

Yes. What you’re looking for is usually called an AI visibility tracker, AI search monitoring, or GEO monitoring tool. These tools repeatedly run relevant prompts through ChatGPT and other AI engines, record whether your brand appears, and alert you when visibility changes. TechnologyAdvice Otterly

Tools worth looking at

• otterly.ai — Tracks brand mentions, citations, sentiment, share of voice, and prompt-level changes across ChatGPT and several other AI engines. It specifically advertises alerts when brand mentions change. Otterly Otterly
• siftly.ai — Particularly focused on ChatGPT. It tracks when your brand is mentioned/recommended, where it appears in an answer, competitors mentioned instead, and provides real-time alerts and drop detection. Siftly
• nightwatch.io — Monitors ChatGPT, Claude, Gemini and Perplexity and explicitly offers alerts when ChatGPT/other LLMs start or stop recommending your brand. Nightwatch
• tryprofound.com — More enterprise-oriented, with broader AI-search analytics and prompt monitoring. Lectern
• ahrefs.com — Useful for quickly checking current AI visibility; its Brand Radar product supports ongoing monitoring and custom prompts. Ahrefs

For your exact requirement

If the requirement is literally:

“Tell me when ChatGPT starts mentioning my brand, and alert me when it stops.”

I'd look particularly closely at Siftly and Nightwatch, because both explicitly describe change/drop alerts rather than merely giving you a periodic visibility report. Siftly Nightwatch

One important nuance: these services generally don't monitor every ChatGPT conversation on the internet. They periodically run a defined set of prompts (e.g., “best accounting software for startups”, “alternatives to X”, “best X for small businesses”) and measure whether your brand appears in those generated answers. Otterly BlueJar

If you tell me your brand + industry + roughly how much you'd spend/month, I can narrow these down to the 2–3 most appropriate options and compare their pricing, alerting, ChatGPT coverage, and API capabilities.

If you mean “we publish content, and we want to know when ChatGPT/Claude/Gemini/other assistants reuse exact sentences or small passages from us, with attribution down to the snippet level”, the tooling is still emerging. There are a few categories:

1. AI citation / content attribution monitoring platforms

These track whether AI assistants mention, cite, or surface your pages, but many focus on URL-level attribution rather than exact sentence reuse.

• Profound — tracks brand visibility and citations in AI-generated answers.
• Peec AI — monitors how brands appear in AI search results.
• Otterly.AI — tracks mentions and citations across AI search experiences.
• Citations.io — focuses on measuring when AI assistants cite or mention brands and sources. Citations.io

These are useful for “did an assistant cite my article?”, but they usually do not prove “this exact sentence came from paragraph 4 of my page.”

2. Content telemetry / attribution infrastructure (closer to your use case)

For snippet-level provenance, look for systems that capture the chain:

your content → retrieved by agent → placed into context → cited → shown to user

• OpenAttribution is building a content telemetry approach intended to record retrieval, grounding, citation, and engagement events. It includes concepts like cited excerpts and token-level usage metadata when supported by agents. OpenAttribution

This is closer to what publishers and content owners need, but it depends on AI agents adopting the telemetry protocol.

3. Build-your-own snippet attribution stack

For the most exact tracking today, companies often combine:

• Document fingerprinting

• Store every paragraph/sentence as an indexed chunk.
• Generate embeddings and hashes for each chunk.
• Track versions over time.
• AI answer capture

• Periodically query assistants with representative prompts.
• Save the complete answers and citations.
• Similarity matching

• Use semantic search + exact matching to find:

• verbatim reuse
• lightly edited reuse
• paraphrases

Typical components:

• vector databases (e.g., Pinecone, Weaviate)
• plagiarism-style detectors
• embedding models
• your own crawler/log pipeline

This approach can produce reports like:

Assistant response sentence: “X reduces onboarding time by 40%...”
Match: 96% similarity
Source: /blog/customer-onboarding-study
Original location: paragraph 7
Attribution provided: no

4. If your goal is legal/licensing enforcement

You may want a content provenance system rather than AI visibility software:

• cryptographic content fingerprints
• timestamped archives
• rights-management metadata
• crawler logs showing retrieval events

These create evidence that content existed and was reused, though they do not automatically show that a model’s internal generation came from your text.

Practical shortlist

One important distinction: citation tracking ≠ reuse detection. An assistant can reuse wording without citing you, and a citation can point to your page without showing that a specific sentence was copied. For true snippet-level attribution, you generally need passage-level matching plus answer capture. OpenAttribution Citations.io

If you tell me whether you need this for SEO/AI visibility, publisher rights enforcement, or enterprise content licensing, I can narrow the tool options.

One important distinction: citation tracking ≠ reuse detection. An assistant can reuse wording without citing you, and a citation can point to your page without showing that a specific sentence was copied. For true snippet-level attribution, you generally need passage-level matching plus answer capture.

If you mean “which tool tells me exactly where my brand/content appears inside a ChatGPT answer for a given query?”, you’re looking for a ChatGPT / AI visibility rank tracker, rather than a traditional SEO rank tracker.

A few tools specifically track this:

• Rankability — tracks mention position, citation position, competitors, and the actual ChatGPT response for tracked queries. Rankability
• RankBits — tracks brand mentions, citations, citation rank, and answer placement across ChatGPT and other AI search engines. RankBits
• Rankscale — tracks answer positions, mentions, citations, and share of voice across ChatGPT and other LLMs. Rankscale.ai
• Wellows — reports the position where your brand/domain is cited for each query and tracks changes over time. Wellows
• refd — measures first-mention position, prominence, citations, and share of voice from repeated ChatGPT runs. refd

Important distinction

There are actually two different “positions” you might want to measure:

1. Mention position — e.g. “Your company is the 2nd brand mentioned in the answer.”
2. Citation/source position — e.g. “Your article is the 3rd cited source.”

These aren't necessarily the same. AI answers don't have a stable equivalent of Google's traditional #1–#10 SERP positions, and the answer can change between runs. SERPclimber Rank.ai

If your goal is specifically “I have a piece of content on my website; tell me which ChatGPT queries surface it and exactly where it appears in the generated answer”, I would focus on tools that expose prompt → full answer → mention/citation → position → cited URL rather than just an aggregate AI visibility score. Rankability and refd explicitly describe those measurements. Rankability refd

If you tell me whether you're trying to track your own website's content, competitor content, or build this capability into your own product, I can point you to the appropriate tool/API architecture.

If you mean “I want a log of every time an AI assistant cites our articles/pages as sources, with timestamps”, you’re looking for an AI visibility / Generative Engine Optimization (GEO) monitoring tool rather than a traditional analytics platform.

A few options:

My recommendation depends on your goal:

• You need an audit trail of “AI cited our content on date X” → start with Citations.io or CitePrism. They are closest to a citation ledger. Citations CitePrism
• You run SEO/content at scale and want dashboards for many prompts and competitors → look at Promptwatch or Profound. Best GEO Software
• You mainly care about ChatGPT/Perplexity/Gemini visibility for buyer questions → tools like MentionScout are designed around repeated prompt monitoring. MentionScout

One important caveat: no tool can see private user conversations with ChatGPT or other assistants. They generally work by running a defined set of prompts on a schedule, capturing the returned answers, and recording whether your content was mentioned or cited. CitePrism

For a content team, I’d also make sure whichever tool you choose stores:

• timestamp of the query/run
• exact prompt used
• model/engine queried
• full AI response snapshot
• cited URL(s)
• your cited page(s)
• citation frequency over time
• export/API access for your own reporting

If you tell me whether you care about ChatGPT specifically, all AI engines, or tracking citations of individual URLs/articles, I can narrow it down.

If your primary problem is “tell me when ChatGPT/Gemini/Perplexity/etc. says something false or damaging about my brand, and show me exactly what was said and where it came from,” I’d focus less on generic AI-visibility tools and more on accuracy/hallucination monitoring.

Based on the current feature sets, these are the main options:

The important distinction

Profound explicitly offers a FactCheck capability that identifies inaccurate claims about your brand and their sources, alongside sentiment, visibility and citation analysis. Profound

Scrunch is particularly interesting if you want to monitor lots of AI platforms. It says it monitors nine platforms, including ChatGPT, Perplexity, Gemini, Claude, Copilot, Grok and Meta AI, and provides response-level sentiment, citations and competitive analysis. Scrunch Help Center Scrunch Its feature set also specifically includes AI-hallucination detection for incorrect or misleading brand mentions. G2

OtterlyAI is more oriented toward scalable AI-search analytics: it tracks mentions, sentiment, share of voice and citations across seven AI engines and lets you inspect the actual answers. Otterly It also says its collection method interacts directly with public AI interfaces rather than relying solely on APIs, which is useful if your goal is to see what users actually encounter. Otterly AI

What I'd look for in your specific use case

Don't buy based solely on a "share of voice" dashboard. For negative/inaccurate brand mentions, the critical workflow is:

Prompt → AI answer → problematic claim → classification (false/outdated/negative) → citation/source → evidence showing the correct information → alert → trend over time

You ideally want:

• Actual answer capture, not just a mention count.
• Claim-level accuracy detection, rather than sentiment alone.
• Citation/source identification, so you can determine what is causing the bad answer.
• Historical snapshots, because AI answers can change.
• Alerts when a previously accurate answer becomes inaccurate/negative.
• Prompt discovery, so you're not only monitoring questions your marketing team already knows about.
• Coverage across ChatGPT, Gemini, Perplexity, Claude, Google AI Overviews/AI Mode, Copilot, etc.
• Competitor comparison, particularly when the model recommends a competitor instead of you.
• API/export capability if you want to feed findings into your reputation, PR, SEO or CRM workflows.

If inaccurate claims are the central requirement, I would put Profound and Scrunch at the top of your evaluation rather than choosing a tool purely on general AI visibility. If the requirement is primarily inexpensive, broad monitoring of mentions/sentiment/citations, OtterlyAI is worth evaluating.

One caveat: these platforms generally monitor sampled prompts, not every possible conversation an AI model has with users. So "no detected negative mention" should not be interpreted as "AI never says anything negative about us."

If you tell me your company size, approximate number of brands/products, AI platforms you care about, and whether you need alerts/API/enterprise security, I can build a more detailed Profound vs. Scrunch vs. Otterly comparison, including current pricing and the specific accuracy-monitoring workflows each supports.

OtterlyAI is more oriented toward scalable AI-search analytics: it tracks mentions, sentiment, share of voice and citations across seven AI engines and lets you inspect the actual answers. otterly.ai It also says its collection method interacts directly with public AI interfaces rather than relying solely on APIs, which is useful if your goal is to see what users actually encounter. help.otterly.ai

What I'd look for in your specific use case

Don't buy based solely on a "share of voice" dashboard. For negative/inaccurate brand mentions, the critical workflow is:

Prompt → AI answer → problematic claim → classification (false/outdated/negative) → citation/source → evidence showing the correct information → alert → trend over time

You ideally want:

Yes — the category you’re looking for is AI visibility / LLM brand-monitoring software. These tools repeatedly give the same customer-style prompts to multiple AI assistants, capture the responses, and let you compare how each assistant describes, recommends, cites, or omits your brand over time.

A few that fit particularly well:

• searchscope.io — especially close to your use case. It runs the same questions across ChatGPT, Claude, Gemini, Perplexity, Google AI, etc. on a recurring schedule, preserving historical runs so you can see how answers change. Searchscope
• querytrace.co — explicitly tracks how ChatGPT, Claude, Gemini, Perplexity, Copilot and Meta AI mention your company, including competitor comparisons and mention-rate trends over time. QueryTrace
• deepsmith.ai — provides per-engine visibility and citation trends over 7/30/90-day periods, plus competitor tracking. DeepSmith
• ahrefs.com — broader coverage including ChatGPT, Gemini, Perplexity, Copilot and Google AI; useful for measuring mentions, citations, topics and cited sources. Ahrefs
• openlens.com — lets you inspect the actual responses, compare platforms, track competitors, and see visibility/trends over repeated query runs. OpenLens

For your specific requirement

I'd look for a tool that gives you a matrix like:

…and then lets you compare that same matrix month-over-month, rather than just giving you one aggregate "visibility score."

If the goal is specifically "show me exactly how ChatGPT vs Claude vs Gemini portray our brand, and highlight what changed since last month," Searchscope, QueryTrace, and OpenLens are particularly aligned with that workflow. Searchscope QueryTrace OpenLens

If you tell me your brand/category and whether this is for marketing, PR, or competitive intelligence, I can narrow this to the tools that best support that workflow and compare their pricing/features.

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

For a marketing team specifically trying to answer “Which of our content/pages are AI assistants actually surfacing for our brand and category keywords?”, I’d look at:

• ahrefs.com — particularly useful if your team already uses Ahrefs. It tracks mentions and citations across ChatGPT, Gemini, Perplexity, Copilot, Google AI Overviews/AI Mode, and identifies the specific domains and pages being cited. Ahrefs
• semrush.com — a good fit if you want AI visibility tied into your existing SEO/content workflow. Its AI-visibility tooling tracks where brands appear, competitors, and content gaps. Semrush
• mentionscout.com — more narrowly focused on AI answers. It tracks buyer prompts across ChatGPT, Claude, Gemini, Perplexity, Grok and Google AI surfaces and exposes the sources/citations behind answers. MentionScout
• getveritas.io — useful if the emphasis is specifically on keyword-level visibility, citations, share of voice, and competitor comparisons across major AI engines. Veritas

What I'd measure

Rather than just “AI mentions,” I'd set up a dashboard around:

If your key question is literally “which content gets surfaced most for our target keywords,” I'd start with Ahrefs Brand Radar or MentionScout. Ahrefs has particularly explicit page-level citation reporting, while MentionScout is more purpose-built around seeing the underlying AI answers and sources. Ahrefs MentionScout

If you tell me your company size, approximate number of keywords, and whether you already use Semrush/Ahrefs, I can narrow this to the 2–3 tools that fit your workflow and budget.

Yes. There’s now a growing category of AI citation / AI visibility monitoring tools, but there’s an important distinction:

• Citation tracking: Did ChatGPT/Claude/Gemini/Perplexity cite your product page?
• Citation accuracy: Does the cited page actually support the claim the AI made?
• Product accuracy: Did the assistant correctly describe your product’s features, pricing, compatibility, specs, etc.?
• Source attribution: Is the assistant actually using your page, or merely citing it while making an unsupported inference?

For what you described, I’d specifically look for the second and third capabilities rather than a conventional “AI visibility” tracker.

Tools worth looking at

• CitePulse explicitly says it flags when AI makes false or outdated statements about a company in addition to tracking citations. CitePulse
• CitationWorks tracks citations and says it monitors accuracy issues alongside mentions, citations, competitors, and sentiment. CitationWorks
• LLM.co offers citation/source attribution monitoring and specifically describes flagging hallucinated citations and misrepresentation. LLM.co
• Novaverb is interesting if you care about the distinction between mentioning your brand and actually citing one of your URLs. It records the answer and the provider/model for each check. Novaverb
• CitePrism provides the underlying AI answers and the sources they used, which makes it useful for manually auditing whether the citation actually supports the answer. CitePrism
• Lighthouse goes more page-level, including scoring individual pages for “AI citability” and mapping citations back to your domain. Lighthouse

But there may be a gap in the market

If by “accuracy” you mean something like:

AI says: “Product X supports integrations with Salesforce and HubSpot.”
Citation: yoursite.com/products/x
Evaluator: “Salesforce is supported by the cited page; HubSpot is not mentioned → partially supported.”

That's a more sophisticated problem than ordinary AI visibility monitoring.

You'd essentially want a citation entailment / source-grounding evaluator that:

1. Runs a controlled set of buyer prompts against multiple AI assistants.
2. Captures the complete answer and every cited URL.
3. Fetches the cited product page.
4. Extracts the claims made about your product.
5. Checks whether each claim is actually supported by the cited page.
6. Separates supported / partially supported / unsupported / contradicted / outdated.
7. Tracks those results over time and across models.
8. Ideally alerts your team when an assistant starts misrepresenting a product.

That's particularly valuable for companies with large catalogs, because “AI cited my page” isn't necessarily a positive signal if the citation is being used to substantiate an incorrect claim.

If you're asking because you're considering building this for your own product/company, I can also and identify exactly what appears to be missing from the current tooling.

That's particularly valuable for companies with large catalogs, because “AI cited my page” isn't necessarily a positive signal if the citation is being used to substantiate an incorrect claim.

If you're asking because you're considering building this for your own product/company, I can also map out the existing competitors and identify exactly what appears to be missing from the current tooling.

If your main KPI is “How often does our product get mentioned/recommended in AI answers versus competitors?”, you’re looking for an AI visibility / AEO (answer-engine optimization) tracker.

The strongest shortlist I’d evaluate is:

• Profound — strongest fit for larger companies that want deep competitive and citation analytics across many AI surfaces. It tracks brand mentions, competitor visibility, prompts, and source citations. TechnologyAdvice Marqeable
• Peec AI — good middle ground if you primarily want clean share-of-voice, mention, citation, and competitor reporting without an enterprise-heavy setup. Marqeable MentionsAPI
• Otterly.AI — worth considering if you want to start relatively inexpensively and monitor multiple AI engines. Current comparisons put entry pricing around $29/month, with higher tiers for more prompts/coverage. Marqeable
• Semrush AI Visibility / AI Toolkit — particularly attractive if you're already a Semrush customer and want AI visibility alongside conventional SEO, rather than buying a separate platform. Semrush
• Ahrefs Brand Radar — another option if Ahrefs is already part of your stack; it adds AI-visibility measurement to traditional SEO research. TechnologyAdvice

What I'd measure

Don't just buy the tool with the biggest "visibility score." Make sure it exposes the underlying measurements:

That distinction matters: being mentioned isn't the same as being recommended, and being recommended isn't the same as being cited. Austin Heaton

My practical recommendation

If you're a mid-market product/marketing team, I'd put Peec + Otterly + Semrush through a trial using the exact same 50–100 buyer prompts and compare their raw answers.

If you're enterprise, I'd include Profound in that bake-off.

The most important test isn't which dashboard looks best. Run identical prompts such as:

• “Best [product category] for [use case]”
• “[Competitor] alternatives”
• “[Your product] vs [competitor]”
• “What software should a [buyer persona] use for [job]?”
• “Best products for [high-intent problem]”

Then calculate:

Your share of voice = your appearances ÷ total brand appearances across the tracked answers

That gives you a much more useful competitive KPI than a vendor-specific “AI visibility score.” Marqeable Austin Heaton

If you tell me your product category, roughly how many competitors you want to track, and whether you're B2B or B2C, I can narrow this to 2–3 tools and compare their current pricing, AI engines, prompt limits, competitor tracking, API/reporting, and strengths/weaknesses.

Yes. There are several platforms now that track AI Share of Voice (AI SOV) — essentially, how often your brand appears in AI-generated answers compared with competitors, and how that changes over time. The category is often also called AI visibility, LLM visibility, or Generative Engine Optimization (GEO) analytics. Siftly Semrush

Some options:

A typical dashboard will let you answer questions like:

• “When someone asks AI ‘best software for X industry’, how often are we mentioned?”
• “Are competitors gaining share in ChatGPT answers?”
• “Which prompts/categories are we losing?”
• “Did our content or PR campaigns increase AI visibility?”
• “How does our AI presence compare with our traditional SEO market position?”

A few things to check before choosing:

• Prompt coverage: Does it let you define the actual buyer questions in your industry, or only generic prompts?
• Competitor tracking: Can you add your real competitive set?
• Model coverage: Does it include the AI surfaces your customers use (ChatGPT, Claude, Gemini, Perplexity, Google AI results)?
• Historical baseline: Many tools only start collecting data after signup, so “trends” may not mean years of historical data. Presenc AI

If you tell me your industry (e.g., SaaS, healthcare, finance, ecommerce, agency, travel) and whether you care more about competitor benchmarking or measuring your own GEO efforts, I can narrow this down.

Yes. The category is generally called AI visibility monitoring, GEO (Generative Engine Optimization), or AEO (Answer Engine Optimization).

For enterprise teams, several platforms specifically track whether your brand appears in AI-generated answers and benchmark that visibility against competitors:

• Profound — enterprise-oriented; tracks prompts, brand mentions, citations, competitors, sentiment, and visibility across multiple AI/search experiences. It also offers prompt-volume and agent analytics. Baarely TechnologyAdvice
• Peec AI — particularly focused on visibility analytics and side-by-side competitor benchmarking, including prompt-level comparisons and trends. AI Search Tools Toolsolved
• Scrunch AI — combines prompt/citation monitoring with competitor benchmarking and AI crawler/agent intelligence; it has an enterprise orientation. TechnologyAdvice Toolsolved
• Otterly.AI — more accessible monitoring, with competitor comparisons across major AI engines, though generally less enterprise-depth than the platforms above. TechnologyAdvice
• Promptwatch — interesting if you want to go beyond measurement: it combines visibility/competitor tracking with answer-gap analysis, content generation, crawler data, and APIs. Toolsolved Toolsolved

What an enterprise-grade system should measure

If you're evaluating these for a serious marketing/brand team, I'd look for:

The important distinction is that “does ChatGPT mention us?” is now fairly commoditized. The more interesting enterprise question is “where are competitors systematically winning AI answers, why are they winning them, and can we connect those gaps to business outcomes?” Current comparisons show the market increasingly differentiating on competitor gap analysis, citation/source intelligence, crawler/agent analytics, and attribution rather than basic mention tracking. Toolsolved Toolsolved

If you're evaluating this as a buyer, I can also give you a side-by-side of Profound vs. Peec vs. Scrunch vs. Promptwatch, specifically around enterprise benchmarking, data/API access, coverage, and approximate pricing.

The category you’re looking for is usually called AI visibility tracking, LLM visibility monitoring, GEO (Generative Engine Optimization) tracking, or AEO (Answer Engine Optimization) analytics. These tools measure how often and how prominently your brand appears in answers from assistants like ChatGPT, Gemini, Claude, Perplexity, and Copilot. Ahrefs Viaudit

Some tools to evaluate:

When comparing vendors, look for these metrics:

• Mention rate — % of relevant AI answers that include your brand.
• Share of voice — your mentions compared with competitors.
• Citation rate — how often assistants cite your website or trusted sources.
• Answer position — whether you appear early in recommendations.
• Prompt coverage — whether the tracked questions match real customer journeys.
• Assistant coverage — ChatGPT, Claude, Gemini, Perplexity, Copilot, and AI search surfaces.
• Historical trends — whether visibility is improving or declining.

For a brand team, a practical setup is often:

• Enterprise: Profound or similar enterprise platforms.
• SEO-led teams: Ahrefs Brand Radar/AI visibility features.
• Smaller teams testing the space: Ahrefs’ free checker, ViAudit, or similar lightweight trackers. Ahrefs Viaudit

If you tell me your company size (startup, mid-market, enterprise), industry, and whether you care more about brand awareness, lead generation, or competitive share of voice, I can narrow the shortlist.

Yes. There are now several platforms specifically aimed at agencies selling AI visibility / GEO (Generative Engine Optimization) services with multi-client dashboards, AI Share of Voice tracking, and white-label reporting. The category is still emerging, but these are the closest fits:

A few things to watch when evaluating them:

Metrics that matter for client reporting

A useful agency dashboard usually includes:

• AI Share of Voice (SoV)

% of tracked AI answers where the client appears vs competitors

• Mention rate

• “How often is the brand named?”
• Position/rank

• Whether the brand is recommended first, second, etc.
• Citation footprint

• Which websites AI engines rely on when mentioning the brand
• Sentiment/context

• Whether mentions are positive, neutral, or negative
• Prompt coverage

• Which buyer questions are being tracked
• Trend reporting

• Month-over-month movement

If you are building an agency offer

The closest analog is probably an SEO reporting stack (Semrush Agency, AgencyAnalytics, Looker Studio) but for AI answers. The winning workflow tends to be:

1. Add client + competitors
2. Define 50–200 buyer prompts
3. Track ChatGPT / Gemini / Claude / Perplexity responses
4. Calculate SoV and citation gaps
5. Deliver monthly branded report
6. Tie improvements to content, PR, and technical changes

If your requirement is specifically “clients log into reports.youragency.com and never see the underlying vendor”, prioritize platforms that offer true client portals (for example Geneo, ReachLLM, Rankfender). If PDF/deck white-labeling is enough, there are more options. Geneo ReachLLM RankFender

I can also put together a comparison by price, API availability, number of clients supported, and whether they allow full SaaS resale/true white-labeling if you're evaluating this as an agency product.

If your core requirement is AI share of voice (SOV) across countries and languages, I’d shortlist these:

• tryprofound.com — Strong fit for enterprise/global competitive intelligence. Its current dataset spans 1.9B+ real-user conversations, 50+ industries, and multiple answer engines; its 2026 research specifically examines cross-market visibility. Profound
• enterprise.semrush.com — Particularly compelling if you already use Semrush. It advertises 15 regional AI-search databases, 289M prompts, competitor benchmarking, and drill-downs from global SOV to market/city/product/prompt. Semrush for Enterprise
• llmpulse.ai — Very explicitly designed for country + language comparisons. It supports multiple countries/languages in the same project, portfolio-level SOV, and BI/API integrations including Power BI and Looker Studio. LLM Pulse
• demandsphere.com — Interesting if you want to combine traditional search SOV and AI SOV. It covers 200+ traditional-search markets plus LLM visibility, with mentions, citations, sentiment and SOV. DemandSphere DemandSphere
• seorce.com — Built around multinational brands, with native-language monitoring across 14 languages, regional competitor SOV, and a consolidated HQ view. Seorce
• searchable.com — Enterprise-oriented tracking across models, markets and prompts, including SOV/citation share and competitor benchmarking. Searchable

How I'd narrow it

One important caveat: “share of voice” isn't standardized across vendors. Different platforms can weight mentions, position, citations, prompts, models, or response frequency differently. Verity Score

For a serious global measurement program, I'd therefore evaluate vendors on methodology before dashboard quality: can they run genuinely localized prompts, rather than merely translating a US prompt? Can you hold the prompt set constant across markets? Can you compare brand mention SOV vs citation SOV separately? And can you export the underlying response/citation data?

If you tell me your number of markets/languages, industry, and approximate monthly prompt volume, I can narrow this to a 3-vendor shortlist and compare coverage, methodology, integrations, and likely pricing.

If your requirement is specifically “which URLs on each client’s site are being cited by AI assistants, and how often?”, I’d shortlist these:

1. otterly.ai — probably the closest fit

OtterlyAI explicitly tracks website citations down to the URL level, including how often each URL is cited and which AI engine cited it. It covers ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini, Copilot and Claude. Otterly Otterly

It also has an Agent Analytics feature that is particularly interesting for client SEO work: it can show which AI agents actually visit which pages on the client's site, with visits broken down by engine. That lets you distinguish:

• AI crawled/visited /page-a
• AI actually cited /page-a
• How many times /page-a was cited
• Which AI engine cited it

Its citations report can also be filtered by engine, date range and country. Otterly.ai Otterly.ai

For an agency managing multiple client sites, this is the one I'd test first.

2. peec.ai — strong alternative

Peec tracks source citations at both domain and URL level, including citation frequency. It monitors ChatGPT, Gemini, Perplexity, Google AI Overviews and AI Mode, among others. Peec AI Peec AI

Its agency offering is explicitly designed for multiple client brands, with per-client visibility, citation and reporting data. Peec AI

One useful distinction is that Peec separates “used” sources (content informed the answer) from “cited” sources (the URL was actually shown as a citation). Peec AI

My recommendation

For your exact use case, I'd start with OtterlyAI, especially if you're trying to build a report like:

The important thing is that you don't just want an AI “visibility” tracker. You want URL-level citation intelligence. Both tools offer that, but Otterly's combination of citation tracking + actual AI-agent page visits is unusually well aligned with what you're describing. Otterly Otterly.ai

If you're doing this for an SEO/GEO agency with dozens or hundreds of client domains, I can also compare OtterlyAI vs Peec vs Profound vs Semrush AI Visibility specifically on multi-client support, URL-level citation data, API access, white-label reporting, and price.

If your goal is specifically mapping which domains AI answer engines cite for product-related questions, you want a tool that records:

• The exact prompts being tested (e.g. "best standing desk under $500")
• Which AI engine was queried (ChatGPT, Perplexity, Gemini, Claude, AI Overviews)
• Every cited domain and URL
• Which competitors appear alongside you
• Changes over time

For ecommerce brands, I'd prioritize tools like this:

These newer GEO platforms are built specifically around AI citations rather than traditional keyword rankings. They repeatedly ask buying-intent questions across major AI engines and record which domains and pages are cited. OnCited Citlyze RiseMore

If I ran an online store today

I'd combine three layers:

1. Traditional SEO

• Ahrefs or Semrush
• Google Search Console
• AI visibility

• OnCited or Citlyze
• Track 100–500 commercial prompts
• Custom monitoring

• Maintain your own prompt library
• Run weekly checks
• Store:

• cited domains
• cited URLs
• your appearance rate
• competitors
• answer text

This lets you answer questions like:

• Which review sites does ChatGPT trust?
• Which blogs does Perplexity cite?
• Does Gemini favor manufacturer pages or editorial reviews?
• Which comparison pages consistently win citations?

For product-related ecommerce questions

Instead of tracking generic keywords, organize prompts into buying stages:

• Best espresso machine under $500
• Is Brand A better than Brand B?
• Best running shoes for flat feet
• Where should I buy Product X?
• Product X review
• Product X alternatives
• Cheapest Product X
• Product X vs Product Y

You'll often find the same third-party domains repeatedly cited (review publishers, Reddit, YouTube, manufacturer sites, retailer product pages, and expert buying guides). Those recurring domains become targets for your content and digital PR strategy.

If you're a smaller store

A lightweight approach is:

• Ahrefs or Semrush for SEO
• One GEO platform (such as OnCited or Citlyze)
• A spreadsheet or database that logs:

• Prompt
• Engine
• Cited domains
• Your rank/mention
• Competitors
• Date

That combination gives you most of the visibility you'd need without investing in a large enterprise AI-monitoring platform.

If you're managing thousands of SKUs, I can also suggest a workflow that automatically maps AI citation patterns by product category (e.g., "coffee makers," "office chairs," "running shoes") and highlights which domains each answer engine consistently trusts.

If your goal is specifically “which scholarly papers, books, and domains are being cited by AI answers, and how can our university press increase the chance our publications are among those sources?”, I’d separate the market into two categories:

Best fits

• semrush.com — probably the best starting point if your press already has an SEO/content team. It tracks citations and mentions across ChatGPT, Google AI Mode and other AI platforms, and lets you define the prompts you care about. It also exposes cited pages/domains rather than just giving you a generic “AI visibility” score. Semrush Semrush
• tryprofound.com — worth evaluating if you need enterprise-scale, URL-level citation intelligence. Current comparisons describe it as particularly strong for identifying exactly which URLs are cited across multiple AI engines. Nick Lafferty
• otterly.ai — attractive if you want a relatively inexpensive fixed-prompt monitoring system. It is oriented toward repeatedly running your chosen queries and recording citations, which can work well for a press with hundreds of defined research topics. Semrush
• GetIntel — interesting if your primary metric is the actual source list behind AI answers, rather than broader brand/GEO analytics. Its current offering covers ChatGPT, Perplexity, Gemini and Google AI Overviews. GetIntel

The important distinction for a university press

I wouldn't measure only “does the press get mentioned?”. You want a dataset something like:

That distinction matters because AI systems can mention a publisher or scholar without actually using its page as a source. Conversely, a paper may be cited without its publisher being named prominently. Current research on AI citation measurement explicitly distinguishes citation selection from the extent to which the cited material actually influences the generated answer. arXiv

For an academic publisher, I'd also build a small in-house benchmark

Rather than relying entirely on a vendor's aggregate score, create perhaps 200–1,000 recurring scholarly prompts covering your catalog:

• “What are the major theories of X?”
• “Which books should I read on X?”
• “What are the foundational papers on X?”
• “Who are the leading scholars working on X?”
• “What evidence exists for X?”
• “Compare the major interpretations of X.”
• Queries corresponding to undergraduate and graduate syllabi.
• Queries corresponding to topics represented heavily in your backlist.

Run them periodically across the major answer engines and save the actual cited URLs. That gives you something much more actionable than conventional SEO rankings.

There is good reason to do this at the domain and page level: a 2026 study using more than 21,000 search-layer citations found substantial differences between platforms in citation breadth and influence, with ChatGPT citing fewer sources but showing higher average citation influence among pages it retrieved. arXiv

One caveat

Don't interpret AI citations as equivalent to scholarly citations. AI citation behavior is still quite variable, and research has documented incorrect, duplicated, and sometimes irrelevant references in chatbot-generated bibliographies. Sage Journals Sage Journals

So for a university press, I'd make the KPI “accurate AI discoverability of our scholarly content”, not simply “number of AI citations.”

My practical shortlist: start by testing Semrush + Profound + Otterly against the same 50–100 academic prompts. If your main requirement turns out to be paper/book/domain-level source extraction rather than general GEO, I'd weight the comparison heavily toward the tools that expose the raw citation URLs and let you export them.

If you tell me roughly how many titles your press has and whether you're trying to monitor books, journal articles, or both, I can design a concrete AI-citation monitoring stack and benchmark methodology for a university press.

Yes. What you’re describing is a more specific need than ordinary “AI visibility”: you want to trace the chain query → AI answer → cited URL → your content, ideally with the exact answer/context that caused the citation.

Tools worth looking at

• otterly.ai — probably the closest match to what you described. Its citation reporting lets you drill from a cited URL to the prompts it appeared in and the exact AI answer, so you can inspect the path rather than just seeing “you got 12 citations.” It tracks ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini, Copilot and Claude. Otterly Otterly AI
• citationly.ai — emphasizes a citation ledger connecting the triggering question, AI provider, cited URL, timestamp and surrounding answer context. It also provides page-level citation histories, which is useful if you want to understand which pieces of your content repeatedly get selected. CITATIONLY CITATIONLY
• tryprofound.com — a broader enterprise AI-search monitoring platform. It's worth considering if you want citation tracking alongside larger-scale prompt/brand/competitor monitoring rather than just forensic citation analysis. Current market comparisons list it alongside Otterly as a live-response monitoring tool. Citability

The feature I would specifically look for

Don't settle for a dashboard that only says:

“Your domain was cited 37 times.”

You want something closer to:

User query
   ↓
"What is the best software for X?"
   ↓
AI assistant: ChatGPT
   ↓
Generated answer
   ↓
Citation #3
   ↓
https://yourdomain.com/article-x
   ↓
Why this page was selected / surrounding answer context

Otterly currently appears particularly aligned with that workflow because its Citation Details → “Cited in prompts” flow can take you from the URL to the prompt and then into the corresponding AI answer. Otterly AI

One caveat: these systems generally show the observable citation path, not the model's private internal reasoning. In other words, they can show what query produced what answer and what source was cited, but not a hidden chain-of-thought explaining exactly why the model internally chose your page.

If your goal is specifically “I want to understand which queries cause AI assistants to discover/cite my pages, and inspect those answers one by one,” I'd start by testing Otterly and Citationly side-by-side.

If your specific goal is “show me the exact prompts/queries where an AI model cited my how-to guide, and which URL it cited,” I’d look at AI-visibility tools rather than traditional rank trackers.

Strong options

• otterly.ai — probably the closest match to your requirement. It lets you monitor exact prompts, see the resulting AI answers, and track the specific URLs cited, including position changes over time. Its Prompt Research feature can also discover prompts from a keyword, brand, or URL. Otterly Otterly AI
• peec.ai — useful if you want prompt/answer monitoring plus competitive analysis and citation visibility across multiple AI search surfaces. Marqeable Conbersa
• tryprofound.com — more enterprise-oriented, with deeper citation intelligence and prompt libraries. It's worth considering if you have a large content library or need sophisticated competitive reporting. Marqeable SEO Counselors
• ahrefs.com Brand Radar — particularly interesting if you're already using Ahrefs, since it connects AI citation data with conventional SEO data and competitor research. Marqeable

The important distinction

There are actually two different questions you may be asking:

1. “Which prompts cause AI to cite my guide?”

→ Prompt-level monitoring is what you want.

2. “What prompts might cause AI to discover/cite my guide that I haven't thought of?”

→ You want prompt discovery/query fan-out in addition to monitoring.

For #2, Otterly is particularly interesting because it explicitly offers AI Prompt Research and Query Fan-Out, alongside URL-level citation tracking. Otterly

One caveat: these platforms generally run/monitor a defined set of prompts; they cannot literally reveal the private internal prompt or reasoning that caused an arbitrary ChatGPT response to cite your page. What they can give you is the observable input → AI answer → cited URL relationship.

For a site publishing how-to guides, I'd test Otterly first and specifically look for: exact prompt, model/engine, full response, cited URL, citation position, and historical changes. That gets closest to the “what exact question made my guide appear?” workflow you're describing. Otterly

If your goal is specifically “which GPT-style crawlers hit my site, when did they hit, and how frequently over time,” there are a few good options:

• developers.cloudflare.com — probably the best fit if your site is already on Cloudflare. It identifies crawlers such as GPTBot, OAI-SearchBot, ClaudeBot, and Bytespider, and gives charts for requests over time, by crawler/operator, host, path, and status code. You can also export the data as CSV. Cloudflare Docs Cloudflare Docs
• Ahrefs Bot Analytics — useful if you already use Ahrefs. It tracks AI assistants alongside other bots, with hourly/daily/weekly/monthly charts and crawled-page reports. Ahrefs Help Center
• Airefs — a more specialized AI-crawler product that provides real-time crawl logs, crawler breakdowns, crawl volume over time, and page-level AI attention. Airefs
• Raw server/CDN logs — the most flexible option. You can filter requests by User-Agent such as GPTBot and calculate your own crawl frequency. Cloudflare specifically notes that crawler User-Agent strings can be used to identify and analyze bot activity. Cloudflare

What I'd use

If you're already using Cloudflare, I'd start with AI Crawl Control. It directly answers the question you're asking without having to build log processing:

GPTBot → requests/hour/day → pages crawled → trend over weeks/months

It also distinguishes OpenAI's different crawler types rather than lumping everything into “ChatGPT traffic.” Cloudflare Docs

One important distinction: Google Analytics isn't sufficient for this. AI crawlers generally make server-side HTTP requests rather than executing your site's JavaScript, so their visits won't necessarily appear in GA4. DataEase AI

If you tell me what your site runs behind (Cloudflare, AWS, Vercel, nginx, WordPress, etc.), I can tell you the simplest setup for getting a historical GPTBot/ClaudeBot/Perplexity crawl-frequency graph.

If your goal is specifically “Which AI assistants are crawling my site, which pages are they fetching, how often, and are they actually indexing/using those pages?”, the tooling has gotten much better in 2026.

My shortlist

• developers.cloudflare.com — best overall for crawl monitoring

• Tracks AI crawlers by operator and crawler, including OpenAI, Anthropic, Perplexity, Google and others.
• Breaks activity down by URL/path, crawler, operator, status code, hostname and time.
• Can identify robots.txt violations and export the data.
• Importantly, Cloudflare distinguishes AI Search, AI Assistant/agent, and AI Training traffic rather than treating all AI bots as one category. Cloudflare Docs Cloudflare Docs Cloudflare Docs
• Available across Cloudflare plans, although the free tier has a 24-hour analytics window. Cloudflare Docs
• ahrefs.com — easiest SEO-oriented option

• Server-side tracking of bots, including AI crawlers.
• Lets you see which individual bots visited and which pages they crawled.
• Particularly convenient if you're already using Ahrefs for SEO. Ahrefs Help Center Ahrefs
• Server/access-log analysis — best for maximum fidelity

• Your raw CDN/server logs are still the fundamental source of truth for requests.
• You can identify bots such as GPTBot, OAI-SearchBot, ClaudeBot, Claude-SearchBot, PerplexityBot, etc. Cloudflare Docs
• This is particularly useful if you want your own historical database rather than relying on a SaaS dashboard.

One important distinction

Crawling ≠ indexing ≠ appearing in AI answers.

For example, seeing OAI-SearchBot fetch /my-article proves that an OpenAI search crawler accessed the page. It does not prove that ChatGPT will subsequently cite that article. Likewise, ClaudeBot visiting a page doesn't establish that Claude has indexed or will use it.

A good monitoring stack therefore has three layers:

Cloudflare's current tooling is unusually strong for the first layer because it can drill down to individual crawler and URL activity and expose the data through its GraphQL Analytics API. Cloudflare Docs Cloudflare Docs

If I were setting this up today, I'd use Cloudflare AI Crawl Control + an AI citation/visibility tracker. That gives you both sides of the equation: “AI crawled this page” and “AI actually surfaced/cited this page.”

If you tell me what your site runs on (WordPress, Shopify, Next.js, Cloudflare, AWS, etc.), I can recommend a specific stack and show you exactly how I'd monitor GPTBot vs OAI-SearchBot vs ClaudeBot vs PerplexityBot page-by-page.

Yes. If by “AI share of voice” you mean crawl our site/content, generate a set of relevant prompts, query AI engines, then measure how often our brand/domain/content is mentioned or cited versus competitors, there are several purpose-built platforms.

Strong options

• Profound — enterprise-oriented AI visibility/GEO platform. Tracks brand visibility, mentions and citations across AI search/answer engines, with competitive analysis. It’s one of the more established options if you need an enterprise workflow.
• Semrush AI Visibility Toolkit — useful if you already use Semrush. It tracks AI mentions, citations, visibility and competitor presence across multiple LLMs. Semrush
• Peec AI — focused specifically on AI-search visibility, including brand mentions, citations and competitor monitoring.
• Otterly.AI — tracks how brands appear in AI-generated answers and monitors visibility across major AI platforms.
• Zumi — particularly close to the metric you described: mention rate, share of voice, average position, per-engine reporting, and the ability to trace mentions back to the prompt and cited sources. It says it tracks ChatGPT, Gemini, Perplexity, Claude, Copilot, AI Overviews, AI Mode and others. Zumi
• SearchPrism — interesting if the crawling + answer-level evidence matters. It crawls the site, runs scheduled prompts against ChatGPT/Claude/Gemini/Perplexity, stores the full answers, and counts brand mentions. Prism
• SourceWatch — straightforward AI share-of-voice measurement across ChatGPT, Perplexity, Gemini and Claude, including competitor appearances. SourceWatch
• Kitbase — measures presence rate separately for being mentioned and being cited, then calculates competitive share of voice. Kitbase

One important distinction

There are actually three different metrics vendors call “AI share of voice”:

1. Mention share — how often your brand is named in an AI answer.
2. Citation share — how often your website/domain is used as a source.
3. Content/source share — how often specific pages from your content corpus are retrieved/cited.

If your goal is “is our content actually being surfaced after the AI crawls/retrieves it?”, I'd prioritize #2 and #3 rather than a generic visibility score.

For example, GeoShark explicitly separates whether you're named, cited, recommended or absent, and shows the pages used to construct answers. GeoShark SearchPrism similarly combines site crawling with stored AI answers and citations. Prism

What I'd evaluate in a demo

Ask each vendor whether you can get this exact report:

For each target prompt → which AI engine → did it mention our brand → did it cite our domain → which URL/page did it cite → which competitor domains were cited → position/order in answer → historical change.

Also ask whether prompts are re-run multiple times, since AI responses aren't deterministic. Current research specifically recommends repeated runs and per-engine reporting rather than treating one answer as definitive. Verity Score Machine Relations

If you tell me your approximate company size / enterprise vs SMB and whether you care more about ChatGPT, Google AI Overviews/AI Mode, or all engines, I can narrow this to 3–5 products and compare their crawling, prompt generation, citation tracking, API/export, and pricing.

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

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

mentionscout.com

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

ahrefs.com

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

rank.ai

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

deepsmith.ai

What I'd use

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

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

The key capabilities I'd insist on are:

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

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

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

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

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

My shortlist by team size

1–5 person marketing team

I’d start with:

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

Content-led SaaS / B2B marketing team

Consider:

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

Agency managing multiple brands

Look at:

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

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

The metrics I’d insist on tracking

A good dashboard should show:

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

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

My practical recommendation for a small team

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

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

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

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

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

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

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

Some tools in this space:

A typical GEO workflow looks like:

1. Find the questions people ask AI

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

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

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

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

A few caveats:

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

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

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

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

The category you’re looking for is AI visibility / LLM visibility / GEO (Generative Engine Optimization) tools.

They run buyer-style prompts through ChatGPT, Perplexity, Gemini, Claude, etc., then show where competitors appear, what sources they’re citing, and where your brand/content is absent. TechnologyAdvice Semrush

Tools worth investigating include:

• Ahrefs Brand Radar — identifies AI visibility gaps, competitor mentions, and the domains/pages being cited. Ahrefs
• Profound — tracks AI answers, citations, competitors, and visibility across major AI engines. TechnologyAdvice
• Scrunch — emphasizes citation intelligence, competitor benchmarking, and AI crawler data. TechnologyAdvice
• OtterlyAI — prompt tracking and competitive AI-search analysis. TechnologyAdvice
• Semrush AI Visibility Toolkit — combines AI visibility with existing SEO/content workflows. Semrush

For your specific use case, the key feature to look for is “competitor citation/content gap analysis”, not merely brand mentions.

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

My shortlist

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

For your specific use case

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

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

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

I'd want these dimensions preserved for every observation:

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

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

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

My practical choice

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

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

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

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

If your specific question is “Which content formats are AI assistants citing most?”, I’d look first at hubspot.com.

It has a Content Type view that explicitly breaks AI citations down by formats such as blogs, comparison pages, documentation, and guides, and tracks how that mix changes over time across ChatGPT, Gemini, and Perplexity. HubSpot

A few alternatives:

• seeqly.ai — particularly close to your use case. It categorizes cited sources into ranked lists, reviews, topic guides, how-tos, comparisons, alternatives, case studies, checklists, etc., and shows the exact cited URLs. Seeqly
• semrush.com — stronger if you also want SEO/competitive analysis; it surfaces cited pages and the prompts they're cited for. Semrush
• citations.io — useful for monitoring citations across ChatGPT, Gemini, Perplexity, and Claude, though its emphasis is more on brand/page citation tracking than format-level analysis. Citations

My pick: Seeqly if your primary goal is format intelligence; HubSpot AI Search Sensor if you want a broader marketing analytics product.

One important distinction: these tools generally tell you what gets cited in their observed answer sets, not a universal causal rule that “AI prefers lists over product pages.” Citation patterns vary by query, engine, topic, and time.

Yes. If your goal is “make my locations more likely to be surfaced by ChatGPT, Gemini, Google AI Overviews, Perplexity, etc.”, I’d look beyond traditional citation management and prioritize platforms that combine multi-location listings + local pages + reviews + AI/GEO visibility monitoring.

Tools worth evaluating

• uberall.com — probably the closest match to what you're describing. Its platform manages listings across 150+ directories, local pages, reviews, and has GEO Studio, specifically designed to monitor and improve visibility in AI search. Uberall Uberall
• yext.com — strong for large multi-location organizations that need centralized business data, listings, and location-page management.
• brightlocal.com — particularly useful if you're managing multiple clients and want local rank tracking, audits, citation management, reviews, and reporting. BrightLocal
• semrush.com — makes sense if you're already using Semrush for broader SEO; combines listing management with local ranking/reputation capabilities. BrightLocal
• localclarity.com — geared toward enterprise/multi-location teams, particularly useful for aggregating listings, reviews, local performance data, and governance.

The important distinction

For AI assistants, listing management alone isn't really the whole game. AI systems can construct an answer from many sources, so you want your location's information to be consistent and richly represented across the web. Uberall explicitly describes this as Location Performance Optimization: listings, reviews, local pages, social presence, and AI visibility working together. Uberall Uberall

For each location, I'd want your software stack to help with:

• Accurate entity data: name, address, phone, hours, categories, services, attributes.
• Directory/map distribution: Google, Apple, Bing, Yelp, industry directories, etc.
• Individual location pages: unique content rather than hundreds of near-identical city pages.
• Structured data: LocalBusiness/Organization schema and location-specific entities.
• Reviews: monitoring, responses, sentiment and review acquisition.
• Local relevance: services, neighborhoods served, landmarks, FAQs, products, amenities.
• AI visibility tracking: actual prompts such as “best [service] near [city]” and whether each location is mentioned/cited.
• Source/citation analysis: identifying which websites AI systems are using when they recommend competitors.
• Bulk management: templates, APIs, permissions and automated updates across all locations.

If you're managing a lot of listings

I'd divide the choices roughly like this:

One caveat: no software can guarantee that an AI assistant will recommend a particular business. The practical objective is to improve the quality, consistency, authority and machine-readability of the business's information and then measure whether AI systems actually surface it. Uberall itself notes that AI answers can draw from many sources and that no brand is guaranteed inclusion. Uberall

If you tell me roughly how many locations you manage (e.g. 20, 100, 1,000+) and whether you're an agency managing clients or an in-house team, I can narrow this down to a few platforms and compare their AI visibility, local-page optimization, listings, API/bulk capabilities, and approximate pricing.

If you're a content agency managing many client accounts, I'd look at tools that do more than track whether a brand appears in ChatGPT. You want multi-client workspaces + citation/visibility tracking + actionable content recommendations + client reporting.

Shortlist

My take for a content agency

Profound is probably the most comprehensive option to investigate first. Its Agency Mode lets you maintain separate client workspaces, while its platform tracks visibility across ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini, Claude, Copilot and other answer engines. It also has tooling intended to turn visibility gaps into content/action opportunities. Profound Profound Help Center

Peec AI is particularly interesting if your agency's business model depends on servicing dozens of clients and producing recurring reports. It offers isolated client projects, unlimited client/team seats on its agency plans, branded/shareable dashboards, and API/CSV/Looker Studio connectivity. Peec AI Peec AI

Citations.io is worth a close look if your actual deliverable is "here are the pages/content/citations we need to create or improve." Its agency offering explicitly turns AI-answer gaps into implementation packs covering things such as content briefs, internal links, schema and citation outreach. Citations

Lantern is another interesting fit if you want to move toward automation: it advertises per-client isolation, citation/share-of-voice tracking, and an agent that researches gaps and produces AI-optimized content. Lantern

The key distinction I'd make

If your agency wants to sell "AI visibility reporting", Peec is worth evaluating.

If you want to sell "AI visibility + actually optimizing the client's content to improve it", I'd put Profound, Lantern, and Citations.io higher on the evaluation list.

And if you're managing 20–100+ clients, I'd prioritize these capabilities during a demo:

• Separate workspace for every client
• Bulk prompt/keyword management
• Citation-level analysis — which pages are AI engines actually citing?
• Identification of content gaps, not merely visibility scores
• Content briefs generated from those gaps
• Competitor/citation-source analysis
• White-label dashboards and reports
• API/export capabilities
• Ability to automate weekly/monthly reporting
• Clear separation of client data

One important caveat: AI-answer visibility is still a relatively new measurement category, so I wouldn't select a platform based on its proprietary "AI visibility score" alone. Look underneath the score at the actual prompts, answers, citations, sources and recommended actions.

If you tell me roughly how many clients you manage (e.g. 10, 50, 200) and whether you want monitoring only vs. automated content optimization, I can narrow this to 2–3 platforms and compare their agency pricing/workflows.

Peec AI is particularly interesting if your agency's business model depends on servicing dozens of clients and producing recurring reports. It offers isolated client projects, unlimited client/team seats on its agency plans, branded/shareable dashboards, and API/CSV/Looker Studio connectivity. Peec AI Peec AI

Citations.io is worth a close look if your actual deliverable is "here are the pages/content/citations we need to create or improve." Its agency offering explicitly turns AI-answer gaps into implementation packs covering things such as content briefs, internal links, schema and citation outreach. citations.io

Lantern is another interesting fit if you want to move toward automation: it advertises per-client isolation, citation/share-of-voice tracking, and an agent that researches gaps and produces AI-optimized content. asklantern.com

The key distinction I'd make

If your agency wants to sell "AI visibility reporting", Peec is worth evaluating.

If you want to sell "AI visibility + actually optimizing the client's content to improve it", I'd put Profound, Lantern, and Citations.io higher on the evaluation list.

And if you're managing 20–100+ clients, I'd prioritize these capabilities during a demo:

If you’re a solo founder with no developer, I’d prioritize tools that give you concrete content edits rather than tools that require technical SEO work.

My shortlist

• semrush.com — best all-in-one fit

• Its Content Toolkit can analyze and optimize drafts for factors associated with AI citations.
• Its AI Visibility Toolkit shows where your brand is mentioned/cited across ChatGPT, Gemini, Perplexity and Google AI, plus which pages are generating citations.
• It also audits whether AI crawlers can access your site. Semrush Semrush
• There’s a free AI visibility checker, so you can test your site before paying. Semrush
• The paid AI Visibility base plan is currently listed at $99/month when billed annually. Semrush
• ahrefs.com — best if you also care about conventional SEO

• Its Brand Radar/AI visibility tools distinguish between being found by AI and actually being cited, which is an important distinction.
• Its free AI Visibility Checker covers ChatGPT, Gemini, Perplexity, Copilot and Google AI results. Ahrefs Ahrefs Help Center

What I'd do in your position

Start with Semrush's free checker and look at:

1. Which pages AI systems already cite.
2. Which questions/prompts mention competitors but not you.
3. Whether AI crawlers can access your important pages.
4. Then use its content optimizer to rewrite your highest-value pages around those gaps.

One important caveat: no software can guarantee that ChatGPT or another AI assistant will cite you. Citation selection depends on the retrieval system, query, competing sources, freshness, authority, and other factors. Ahrefs' research, for example, found that AI systems often favor relatively fresh content, while Semrush specifically recommends improving structure, clarity and entity signals. Ahrefs Semrush

If you want the simplest possible setup: I'd use Semrush + your existing CMS + ChatGPT rather than buying a pile of specialized "GEO/AEO" tools.

If you give me your website URL, I can also look at it and tell you exactly what I'd change first to make its content more citeable by AI assistants, without requiring a developer.

If your goal is specifically getting educational guides cited by AI systems—and monitoring whether that happens responsibly, I’d look at OtterlyAI first. It combines prompt research, AI-answer monitoring, citation tracking, and optimization across ChatGPT, Perplexity, Gemini, Google AI experiences, Copilot, and Claude. Otterly Otterly

otterly.ai

For an educational publisher, I’d evaluate platforms on four things:

• Citation-level monitoring: Can you see the exact guide/page an AI answer cited, rather than merely seeing that your brand was mentioned?
• Cross-engine coverage: Citation behavior differs substantially between AI systems, so measuring only ChatGPT can give a misleading picture. arXiv Citare
• Prompt tracking: You want a fixed set of questions corresponding to what students, teachers, or parents actually ask, then rerun them periodically.
• Responsible optimization: The objective should be making your guides clearer, better sourced, structured, and easier to accurately extract—not manufacturing mentions or encouraging models to cite you regardless of relevance.

Two alternatives worth testing are Citations.io, which emphasizes exact cited URLs and implementation recommendations, and CueScout, which explicitly distinguishes a citation from a mere mention. Citations CueScout

For your use case, I'd set up a measurement loop of query → AI answer → cited source → exact passage/page → factual accuracy → change over time. That gives you a much more defensible picture than a generic “AI visibility score.” arXiv

If you tell me roughly how many guides you have and whether you're primarily targeting ChatGPT, Google AI, Perplexity, or all of them, I can narrow this to 2–3 platforms and compare their monitoring/optimization capabilities.

Yes. The category you want is usually called AI visibility / GEO (generative-engine optimization) software. For a news publisher, I’d separate tools that measure citations from tools that can actually recommend how to structure the next story.

Tools worth evaluating

• getvisibly.app — particularly relevant to newsrooms. It focuses specifically on publisher AI visibility and frames the problem around which stories/questions get cited. Visibly
• conductor.com — useful if you already have an enterprise SEO/content operation. Its 2026 publishing research analyzed 13M+ publisher citations across seven AI engines and looks at which page types get cited. Conductor
• yext.com — stronger on measuring and understanding AI citations across ChatGPT, Perplexity, Gemini and Claude. Its 2026 analysis covered 17.2M citations. Yext
• muckrack.com — worth looking at for a newsroom because its GEO guidance is explicitly oriented toward news discovery, citation and attribution rather than generic brand SEO. Muck Rack
• slatehq.com — more measurement/benchmarking oriented; tracks AI visibility across several answer engines and can help establish a baseline for a publisher. Slate

But there's an important distinction

If your requirement is specifically:

"Given this story/topic, tell my editor whether it should be a breaking-news article, explainer, FAQ, timeline, data table, comparison, Q&A, etc., based on which formats AI systems tend to cite."

That's a less mature product category. Most existing platforms measure whether you're being cited rather than acting as an editorial-format recommendation engine.

For a news site, I'd want software with this workflow:

1. Topic detection — identify the questions people are asking AI about a developing story.
2. Competitive citation analysis — query ChatGPT, Perplexity, Gemini, Claude, Google AI Mode, etc., and identify which publications are being cited.
3. Format analysis — classify cited pages as breaking news, explainer, FAQ, timeline, analysis, data visualization, Q&A, live blog, etc.
4. Content-gap analysis — determine what information the cited pages provide that your article doesn't.
5. Brief generation — recommend a structure such as:

• 2-sentence direct answer
• key facts/data
• timeline
• named sources
• methodology
• FAQ
• original reporting
• Post-publication monitoring — repeatedly test the same prompts and show whether the new article starts appearing as a citation.

That last piece matters because citation behavior differs substantially between AI engines; Conductor's research, for example, found persistent differences in source preferences between engines. Conductor

There is also evidence that content format matters: one 2026 experiment reported that comparison/list-style pages represented 51% of its non-homepage citations, although that's a single study rather than a universal rule for newsrooms. Prismic

If I were evaluating vendors for a newsroom

I'd make the RFP less about "GEO score" and ask vendors to demonstrate:

One caution: "AI citation optimization" isn't equivalent to increasing referral traffic. AI assistants can cite a publisher while satisfying the user's question without generating a click. That's already a significant concern for publishers; current reporting describes publishers treating AI visibility as a distinct distribution metric rather than simply another form of SEO traffic. Digiday

If you're building or buying this for a news organization, I can also map out the current vendor landscape and identify which products actually provide editorial/content-format recommendations versus merely reporting AI citation metrics.

If you mean one dashboard that monitors how your website/brand appears across ChatGPT, Perplexity, Gemini, Claude, Google AI Overviews/AI Mode, and Copilot, the main tools to look at are:

• otterly.ai — tracks ChatGPT, Perplexity, Google AI Overviews, Google AI Mode, Gemini, Microsoft Copilot, and Claude, with mentions, citations, competitors, and visibility trends in one dashboard. Otterly Otterly AI
• tryprofound.com — more enterprise-oriented; combines AI visibility, citations, prompt demand, sentiment, and AI crawler/agent analytics. TechnologyAdvice
• llmrefs.com — particularly focused on broad AI-platform coverage; current comparisons report 11 AI surfaces, including ChatGPT, Perplexity, Claude, Gemini, Grok, Meta AI, and DeepSeek. AY Rank
• runtruffle.com — gives a single visibility score plus per-assistant visibility, position, sentiment, citations, and competitor tracking. Truffle

If your requirement is specifically “put my website in once and see how AI assistants describe/recommend/cite it across multiple engines,” OtterlyAI is probably the closest direct match. It explicitly runs your chosen prompts across multiple AI experiences and reports brand mentions and website citations. Otterly

If you tell me your budget and whether you're an agency, SaaS/company, or SEO team, I can compare the top 3 on price, AI engines, citation tracking, competitor tracking, historical trends, and API/reporting.

Yes — what you’re describing is usually called AI visibility tracking, LLM visibility, or GEO/AEO tracking.

These tools monitor whether AI assistants mention/cite your content or brand, which sources they use, and often where you appear relative to competitors.

Strong options

• ahrefs.com — Tracks ChatGPT, Gemini, Perplexity, Copilot, Google AI Overviews/AI Mode, with mention and citation data, historical visibility, competitive benchmarking, and large-scale prompt data. Ahrefs Ahrefs
• rank.ai — More focused specifically on AI visibility: daily prompts across ChatGPT, Claude, Gemini, Perplexity, Grok and others, with share of voice and citation tracking. Rank.ai
• promptwatch.com — Tracks mentions/citations across ChatGPT, Gemini, Perplexity, Claude and other AI platforms, including citation ordering and competitive visibility. Promptwatch
• citevis.com — Particularly relevant if you care about exactly which content gets cited. It tracks mentions, citation position, competitors, share of voice, and the sources AI assistants use. Citevis
• deepsmith.ai — Measures mention rate, citation rate and share of voice and lets you inspect the underlying AI answers and sources. DeepSmith
• clovion.ai — Tracks brand mentions, rankings, citations and sentiment across six AI engines, including position/rank within answers. Clovion AI

The distinction I'd pay attention to

If by “where our content is being used” you mean:

“Which of our articles/pages are ChatGPT, Gemini, Perplexity, etc. actually citing, and how does that compare with competitors?”

then you want citation/source tracking, not merely an AI “visibility score.”

The useful dashboard should ideally give you:

If you're choosing software for a content/SEO team, I'd shortlist Ahrefs, Promptwatch, and Citevis first, depending on whether you prioritize large-scale SEO data, broad AI-engine monitoring, or granular citation/source analysis. The vendors differ substantially in how they generate prompts and measure “rank,” so I wouldn't compare their visibility scores directly. Promptwatch Ahrefs Citevis

If you tell me what kind of content/site you have (SaaS, publisher, ecommerce, agency, etc.) and roughly how many pages/brands you need to track, I can narrow this to 3–4 tools and compare pricing, AI engines, citation tracking, competitor rank, API access, and reporting.

If you mean enterprise-grade tracking of how often your brand appears in ChatGPT/Gemini/Claude/Perplexity/etc., with competitor share-of-voice, citations, prompts, and historical benchmarking, I’d put Profound at the top of the enterprise shortlist.

Current 2026 comparisons consistently position Profound toward enterprise use, while Peec AI and Otterly are generally positioned as lighter/mid-market alternatives. Refine AI Bloomiro Mentn

How I’d shortlist them

Profound's enterprise tier reportedly supports substantially broader engine coverage than its self-serve tiers, including ChatGPT, Perplexity, Gemini, AI Overviews/AI Mode, Copilot, Meta AI, Grok, DeepSeek and Claude. Baarely

The important distinction

For an enterprise, I wouldn't select based simply on "number of AI engines tracked." The more important question is whether the platform gives you a defensible AI Share of Voice (SOV) methodology.

I'd want these metrics:

• Mention rate: % of tracked prompts where your brand appears
• Share of voice: your mentions relative to defined competitors
• Position/share of recommendations: whether you're first/second/etc. when assistants recommend multiple vendors
• Citation share: % of citations attributable to your domains
• Competitor displacement: prompts where competitors appear but you don't
• Prompt-level visibility: visibility by product, use case, industry, geography and funnel stage
• Sentiment/attribute association: what assistants actually say about each brand
• Source/citation analysis: which publishers and pages influence the answers
• Model-by-model variance: because ChatGPT visibility can differ substantially from Gemini, Claude, Perplexity, etc.
• Historical trends: SOV before/after content, PR, SEO or product changes
• Enterprise controls: users, permissions, reporting, APIs, exports and governance

That matters because two vendors can report different "visibility" percentages simply because they use different prompt sets, sampling, weighting and definitions of a mention.

My practical recommendation

For a Fortune 1000 / large B2B enterprise, I'd run a bake-off between:

1. Profound — benchmark for enterprise-grade AI visibility intelligence.
2. Peec AI — compare its analytics and SOV methodology against Profound.
3. AthenaHQ or Scrunch — evaluate if you want more than measurement and care about the optimization/action layer.

Don't buy based on a vendor demo showing a pretty SOV chart. Give each vendor the same 100–500 commercially important prompts, 5–10 competitors, your key geographies, and the same measurement period, then compare the raw answer-level data.

One especially important point: "AI visibility" isn't yet a standardized industry metric. Current tools broadly track mentions/citations and competitor comparisons, but their methodologies differ. Refine AI NeuralGen

If you tell me your industry, approximate number of brands/products, geographies, and whether this is for SEO, brand, competitive intelligence, or executive reporting, I can build you a Profound vs Peec vs Otterly vs AthenaHQ enterprise scorecard with the exact capabilities I'd test in an RFP.

Yes — this category is usually called AI visibility / AEO / GEO tracking. Good options include:

• otterly.ai — tracks prompts across ChatGPT, Perplexity, Gemini, Claude, Copilot and Google AI experiences, including mentions, position, competitors and citations. Otterly Otterly
• ahrefs.com — particularly interesting if you already use Ahrefs; its database is based on hundreds of millions of real/search-derived prompts. Ahrefs
• tryprofound.com — enterprise-oriented AI visibility monitoring.
• peec.ai — focused on tracking brand visibility and competitive positioning across AI search. Conbersa
• modelmention.io — simpler monitoring of visibility, position, share of voice and citations across ChatGPT, Gemini and Perplexity. ModelMention

If your specific goal is “give me the thousands of questions people ask in our category, then tell me whether our brand appears and where it ranks in each AI answer,” I’d narrow the shortlist to Otterly, Ahrefs, Peec, and Profound.

If your use case is client-facing AI visibility reporting—tracking where a brand appears in ChatGPT, Perplexity, Gemini, Google AI Overviews/AI Mode, etc., plus citations, answer position, competitors, and historical trends—I’d shortlist these:

My practical pick: Peec AI

For an agency building repeatable client dashboards, Peec looks particularly well aligned. Its agency offering supports multiple client projects, and its Looker/Data Studio connector lets you build a branded dashboard template and clone it for clients. It also exposes CSV/API data for BI systems such as BigQuery, Tableau and Power BI. Peec AI

It tracks mention rate, average position, citation count and sentiment across ChatGPT, Perplexity, Google AI Overviews, Google AI Mode, Gemini and Microsoft Copilot, with daily updates. Peec AI

peec.ai

If citation-level detail is the priority

OtterlyAI is worth a serious look. It stores the actual AI answers and tracks which URLs were cited, whether the brand was mentioned, competitors, ordering, sentiment and share of voice. Its current analytics product covers seven engines. Otterly

otterly.ai

Profound is the one I'd investigate if you're serving large enterprises and need much deeper research/data infrastructure rather than simply attractive client reports. Current comparisons describe it as the enterprise-oriented option with particularly deep citation and prompt data. Bloomiro BusinessMCP

One important distinction

Don't evaluate these purely on "AI visibility score." For client reporting, I'd want the underlying dataset to include:

• Exact prompt
• AI engine/model
• Date/time sampled
• Full answer
• Brand mentioned?
• Brand position/order
• Competitor positions
• Every cited URL/domain
• Citation frequency
• Share of voice
• Sentiment/context
• Historical changes
• Country/language/device context, where available
• Export/API/Looker support
• White-label client reporting

That distinction matters because two tools can both say a client has "32% AI visibility" while one actually lets you show the client which questions produced that number and which URLs ChatGPT/Perplexity/Gemini cited.

For an agency specifically: I'd start by demoing Peec + Otterly + Profound and ask each vendor to show you the raw prompt → answer → citation → position → historical dashboard workflow, not just their headline visibility score. Current market comparisons put Peec toward the agency/mid-market end, Otterly toward accessible monitoring, and Profound toward enterprise depth. Bloomiro Baarely

If you tell me how many clients/prompts you need to track and whether you want Looker Studio, Power BI, or a native white-label dashboard, I can narrow this to the most suitable 2–3 and compare current pricing/features.

If citation-level detail is the priority

OtterlyAI is worth a serious look. It stores the actual AI answers and tracks which URLs were cited, whether the brand was mentioned, competitors, ordering, sentiment and share of voice. Its current analytics product covers seven engines. otterly.ai

otterly.ai

Profound is the one I'd investigate if you're serving large enterprises and need much deeper research/data infrastructure rather than simply attractive client reports. Current comparisons describe it as the enterprise-oriented option with particularly deep citation and prompt data.

One important distinction

Don't evaluate these purely on "AI visibility score." For client reporting, I'd want the underlying dataset to include:

For a small content studio on a tight budget, I’d start with Praised’s free AI Visibility Checker if you want to check individual articles/pages, rather than just your overall brand. It accepts a specific URL and assesses things like crawlability, structured data, answer-first content, entity grounding, and metadata. It offers free checks with no signup. Praised

praised.co

A couple of good alternatives:

• RankBits — useful if you want to see whether AI actually mentions/cites you for a few buyer-style prompts. Its free scan includes 3 prompts across ChatGPT, Google Search and Google AI Overviews. RankBits
• Ahrefs AI Visibility Checker — better for a brand-level snapshot across ChatGPT, Gemini, Perplexity, Copilot and Google AI Overviews; no signup required. Ahrefs
• Semrush AI Visibility Checker — another free brand/domain-level snapshot, including mentions and pages generating AI citations. Semrush

For your use case: if you're trying to answer “Is this particular article structured so AI systems can understand and cite it?”, use Praised. If you're asking “Does AI actually mention my studio/client when people ask relevant questions?”, use RankBits.

One important distinction: a technical “AI visibility” audit isn't the same as measuring actual AI citations. Some free tools inspect your page's signals without querying live AI answers. Praised cituna.com

Yes. There are now several free AI-visibility checkers aimed at exactly this problem: whether shoppers asking AI assistants for recommendations actually encounter your store or products.

Good free options

• Ecomrank — probably the most directly relevant for a small ecommerce store. Enter your store URL and what you sell; it tests buyer-style questions against ChatGPT, Perplexity, and Google AI Overviews and reports whether you're cited or mentioned. No signup is required. Ecomrank

ecomrank.io

• Wildcard — specifically designed for ecommerce. Its free audit looks at where your products appear across ChatGPT, Gemini, Perplexity, Claude, and Google AI Overviews, including competitor products and information gaps. Wildcard

wild-card.ai

• Ahrefs AI Visibility Checker — free and no signup. It checks your brand across ChatGPT, Gemini, Perplexity, Copilot, Google AI Overviews and AI Mode. It's more brand-focused than individual-product-focused, but useful for getting a broad baseline. Ahrefs

ahrefs.com

• DecodeIQ — interesting if you care specifically about individual product pages. You paste a product URL and it tests shopping questions and reports product mentions across AI answers. The free version currently advertises up to three questions and ChatGPT access. DecodeIQ

One important distinction

There are really two different things you want to measure:

1. Can AI access/read your product page?

This is about crawlers, robots.txt, structured data, page content, etc. Tools such as IsVisible check this. isvisible.ai

2. Does AI actually recommend/cite your product when a shopper asks a buying question?

This is the more valuable ecommerce measurement. For example, you want to know whether asking “What's a good waterproof backpack under $150?” produces your product, rather than merely whether ChatGPT can technically crawl your page.

For a small ecommerce owner, I'd start with Ecomrank or Wildcard, then manually test 5–10 questions that real customers might ask. A product can be perfectly crawlable but still never appear in AI recommendations.

If you tell me what you sell + your store URL, I can also show you exactly which kinds of AI queries I'd test for your products and which free checker is likely to be most useful.

For a free, one-time competitive snapshot across several competitor URLs, I’d start with aisearchvisibility.ai.

• It accepts any public URL, explicitly supports competitor audits, and gives an AI-visibility/citation-readiness score plus specific blockers and fixes. AI Search Visibility
• The free tier currently allows 5 URL audits/month, with no credit card required—so it fits a small competitor sample well. AI Search Visibility
• It’s more appropriate for URL-level competitive research than brand-only visibility checkers.

Another option

ahrefs.com is worth using alongside it if you care more about brand mentions in AI answers than the technical/citation readiness of individual URLs. It checks ChatGPT, Gemini, Perplexity, Copilot, Google AI Overviews and AI Mode without signup. Ahrefs

My suggested workflow: run each competitor URL through AI Search Visibility, then run each competitor's brand through Ahrefs. That gives you two different signals: “Is this page structured/citable?” and “Does AI actually mention this brand?” Ahrefs AI Search Visibility

If your priorities are multiple clients + AI share of voice + polished, client-facing exports, I’d shortlist these:

My take

For a typical SEO/content agency, I'd start with Zumi, Citenzo, and Cliro. Their positioning is particularly aligned with the workflow you described: isolate each client, track competitors and AI visibility, then produce something you can actually hand to the client. Zumi specifically advertises separate client workspaces, agency-branded exports, and up to nine AI engines. Zumi

Citenzo is particularly interesting if the report itself is the product. Its agency plan currently advertises up to 30 client brands, six AI engines, share-of-voice/gap analysis, and white-label reports with your logo and domain. Citenzo

Cliro is worth looking at if you hate report assembly. It supports scheduled reports, white-label branding, PDF/CSV export, share-of-voice reporting, citations, and one report per client. Cliro

Citaim is a good smaller-agency option if five client projects is enough: it explicitly offers per-client workspaces and white-label PDFs containing SOV, trends, competitor comparisons, and cited sources. Citaim

One thing I'd prioritize

Don't choose based purely on the number of AI engines. For agency use, I'd test whether the platform can produce this without manual spreadsheet work:

Client → tracked buyer prompts → AI visibility → share of voice vs. competitors → citations/sources → month-over-month movement → branded PDF

That's the workflow that turns AI visibility tracking into a repeatable agency deliverable.

If you tell me roughly how many clients you manage (e.g. 10, 50, 100+) and whether you need Looker Studio/GA4 integration, I can narrow this to the 2–3 most suitable options and compare current pricing.

For a boutique SEO/content agency, I’d look at this as two separate requirements:

1. Which client pages/domains are actually being cited by AI assistants?
2. Can I turn that data into a repeatable, client-friendly report?

My shortlist

Current 2026 comparisons similarly distinguish dedicated AI-monitoring products such as Otterly, Profound and Peec from SEO suites such as Ahrefs and Semrush. TechnologyAdvice ToolChase

If I were setting up a boutique agency

I'd start with Ahrefs Brand Radar if you're already an Ahrefs customer.

The particularly useful feature for your question is the Cited Pages reporting. Ahrefs can show which URLs are being cited, the prompts/responses associated with those citations, and historical citation performance. It also exposes citation data through an API, which is useful if you want to build your own client reporting layer. Ahrefs

Ahrefs is also unusually useful for answering the client question:

"Which of our pages are AI engines actually using as sources?"

rather than merely:

"Does AI mention our brand?"

Its current AI visibility dataset spans ChatGPT, Gemini, Copilot, Perplexity and Google AI, with hundreds of millions of search-backed prompts. Ahrefs

I'd choose Otterly instead if AI visibility is becoming a standalone agency service. It is purpose-built around prompt tracking, mentions and citations rather than being an SEO suite with AI functionality bolted on. Current agency-oriented comparisons specifically highlight its multi-engine monitoring and reporting capabilities. TechnologyAdvice Otterly

The reporting stack I'd build

For each client, I'd want the monthly report to automatically produce:

• AI citation visibility: % of tracked prompts where the client's domain is cited
• Cited URLs: the actual pages receiving citations
• Citation count: citations by URL and by engine
• Top prompts: questions producing citations
• New/lost citations: month-over-month changes
• Competitor citations: which competing domains are being cited instead
• Engine breakdown: ChatGPT vs. Perplexity vs. Google AI Overviews vs. Gemini, etc.
• Content opportunities: prompts where competitors are cited but the client isn't
• Traditional SEO correlation: organic rankings/traffic for cited vs. non-cited pages

That last piece is particularly valuable for an agency because you can turn "AI visibility" into a content-performance story, rather than giving clients another vanity metric.

One important distinction

Don't buy a tool merely because it reports brand mentions. Make sure it exposes the source URL/page behind the answer.

That's the actionable data. For example:

Prompt: "Best accounting software for small nonprofits?"
AI answer: Client mentioned
Citation: /nonprofit-accounting-guide/

Now you can tell the client which asset is earning AI visibility and why that asset matters. Ahrefs explicitly supports this kind of cited-page analysis. Ahrefs Ahrefs

My practical recommendation:

• Already use Ahrefs → Brand Radar
• Want a dedicated, relatively lightweight agency AI-monitoring product → OtterlyAI
• Building a serious enterprise GEO practice → Profound
• Want maximum control → use an API-based tool and build the reporting in Looker Studio/your BI system

If you tell me roughly how many clients/domains you manage (e.g. 10, 30, 100) and whether you already use Ahrefs/Semrush, I can narrow this down to the most economical setup and show what the client report should look like.

That's the actionable data. For example:

Prompt: "Best accounting software for small nonprofits?"
AI answer: Client mentioned
Citation: /nonprofit-accounting-guide/

Now you can tell the client which asset is earning AI visibility and why that asset matters. Ahrefs explicitly supports this kind of cited-page analysis.

"Does AI mention our brand?"

Its current AI visibility dataset spans ChatGPT, Gemini, Copilot, Perplexity and Google AI, with hundreds of millions of search-backed prompts.

If by “AI answer sentiment data” you mean tracking how ChatGPT/Perplexity/Gemini/etc. describe your brand—mentions, citations, position, sentiment—and then tying that to website traffic, leads, and revenue, I’d shortlist these:

Cairrot is particularly close to what you described: it combines AI visibility, citations, sentiment and LLM traffic with GA4, Search Console and BI data, allowing you to analyze AI-search performance alongside conversions. Cairrot

Indexly explicitly combines AI mentions/sentiment with AI referral traffic and revenue, including the pages receiving AI traffic. Indexly Indexly

Semrush is worth considering if you want a more established SEO platform: its AI visibility product tracks mentions, citations and sentiment and can connect AI-search data to traffic and conversion data through GA4 or Adobe. Semrush Semrush for Enterprise

What I'd actually measure

Don't stop at an “AI visibility score.” Build a funnel like:

AI answer sentiment → AI mention/citation → AI referral traffic → engaged session → lead/signup → revenue

Then segment it by:

• AI engine: ChatGPT vs Gemini vs Perplexity
• Prompt/intention: informational vs comparison vs high-buying-intent
• Sentiment: positive/neutral/negative
• Landing page
• Product/category
• Conversion type and revenue

That lets you answer the much more useful question: “When AI describes us more favorably or mentions us more often, does that actually produce more qualified traffic and conversions?”

One caveat: AI-answer visibility and actual referral traffic are different measurements. Tools that run prompts against LLMs measure potential exposure, while analytics platforms measure people who actually arrive on your site. Plausible Analytics

If you tell me your stack (GA4 + HubSpot/Salesforce, Shopify, etc.) and whether you're B2B or B2C, I can narrow this to 2–3 options and show you the exact dashboard/data model I'd use.

Don't stop at an “AI visibility score.” Build a funnel like:

AI answer sentiment → AI mention/citation → AI referral traffic → engaged session → lead/signup → revenue

Then segment it by:

• AI engine: ChatGPT vs Gemini vs Perplexity
• Prompt/intention: informational vs comparison vs high-buying-intent
• Sentiment: positive/neutral/negative
• Landing page
• Product/category
• Conversion type and revenue

That lets you answer the much more useful question: “When AI describes us more favorably or mentions us more often, does that actually produce more qualified traffic and conversions?”

One caveat: AI-answer visibility and actual referral traffic are different measurements. Tools that run prompts against LLMs measure potential exposure, while analytics platforms measure people who actually arrive on your site.

Yes. What you’re describing is now a fairly specific category: LLM/AI-answer monitoring, but your requirements go a step beyond simple “does ChatGPT mention us?” tracking.

You want something that can answer:

When an AI assistant talks about our services, what does it say about us, how positive/negative is the framing, and what kind of buyer question triggered the response?

Tools worth looking at

• Profound — tracks brand presence, citations, competitors, sentiment, and prompts across AI answer engines. It is geared more toward enterprise-grade AI visibility/brand intelligence. TechnologyAdvice GeoHats
• Semrush AI Visibility Toolkit — particularly relevant to your use case because current descriptions include sentiment tracking and query-intent distribution, alongside mentions/citations and competitor research. Exploding Topics
• Otterly.AI — tracks AI answers, brand mentions, competitors, citations and sentiment. More monitoring-oriented and generally positioned toward marketing/SEO teams. Loudmink TechnologyAdvice
• Peec AI — another AI visibility monitor that focuses heavily on prompt-level results, competitors and how brands appear in AI responses. GeoHats
• Promptwatch — specifically focuses on brand sentiment/tone inside AI-generated answers, rather than conventional social sentiment. Promptwatch

The important distinction

There are actually three different measurements you may want:

The third one is especially useful because you can discover things like:

Recommendation queries: 72% positive framing
Comparison queries: 41% positive framing
Review queries: 63% positive framing
Alternative-to-[competitor] queries: 28% positive framing

That gives you a much more actionable picture than an overall “AI sentiment = 68%” number.

And importantly, intent segmentation isn't universal among these platforms. Semrush's current AI visibility offering explicitly mentions query-intent distribution, while other platforms tend to leave more of the categorization to their prompt/project structure. Exploding Topics TechnologyAdvice

If this is for your own service

I'd structure the measurement around a taxonomy like:

• Recommendation: “What are the best X services?”
• Comparison: “X vs Y”
• Alternative: “Alternatives to X”
• Review: “Is X good?”
• Evaluation: “Is X worth it?”
• Pricing: “How much does X cost?”
• Problem/solution: “How can I solve X?”
• Vendor-specific: “What does [your company] offer?”
• Trust/risk: “Is [company] reputable/reliable?”
• Local: “Best X near me”

Then score each response on mention → position → sentiment/tone → attributes → recommendation strength → competitors mentioned → citations/sources.

If you tell me what kind of services you sell, I can also identify which of these tools comes closest to doing exactly this and what you'd likely have to build yourself.

And importantly, intent segmentation isn't universal among these platforms. Semrush's current AI visibility offering explicitly mentions query-intent distribution, while other platforms tend to leave more of the categorization to their prompt/project structure.

If this is for your own service

I'd structure the measurement around a taxonomy like: