Answers that name a competitor

If you’re a small ecommerce shop and budget is the priority, I’d start with Otterly.AI.

• Otterly.AI — best budget starting point. Current comparisons put its entry plan around $29/month, with tracking for a small set of prompts across ChatGPT, Google AI Overviews, Perplexity, and Copilot. It can show whether your brand is mentioned and which URLs get cited. Loudmink GTM Help
• Peec AI — better if you need substantially more prompts, competitors, reporting, or multiple people working on it, but it starts around $95/month, so I'd consider it later. Loudmink
• Profound — excellent for deep enterprise-level analysis, but overkill for a small shop and much more expensive. Omid Saffari

What I'd actually do

Start with 15–20 high-intent questions, rather than trying to monitor every product page. For example:

• “best [product category] for [use case]”
• “[product category] alternatives”
• “best [product category] under $100”
• “where to buy [product type]”
• “[your product] vs [competitor]”

Then track whether your products are recommended, which competitors appear, and which websites/pages the AI cites. That's much more actionable than simply checking whether your domain gets mentioned.

One caveat: the cheap plans have relatively low prompt limits, and some newer AI engines can cost extra. Loudmink

Bottom line: Otterly.AI for a tight budget. If you tell me roughly how many products you have (e.g. 20, 200, 2,000) and whether you mainly care about ChatGPT, Google AI, or both, I can tell you the cheapest setup I'd use.

For an enterprise looking for historical analytics of how your brand appears across AI models, I would evaluate this as an AI visibility / Generative Engine Optimization (GEO) / Answer Engine Optimization (AEO) intelligence requirement rather than a traditional social listening problem. The strongest platforms typically track:

• Brand mentions and recommendations inside LLM answers
• Share of voice vs. competitors
• Sentiment and positioning
• Citation sources (which websites, articles, communities, etc. influence the answer)
• Prompt-level history over time
• Model-by-model differences (ChatGPT vs. Gemini vs. Claude vs. Perplexity, etc.)
• Alerts when brand perception changes Built In Meltwater

My shortlist for enterprise use:

My recommendation by enterprise scenario

If you are a global brand managing reputation

Start with Profound + Meltwater.

• Profound gives you the AI-answer analytics layer.
• Meltwater gives PR teams a familiar reputation-monitoring workflow.
• Together they cover “What are AI models saying?” and “How does this relate to broader brand perception?” Built In Meltwater

If your goal is marketing growth and AI search acquisition

Look at Profound, Scrunch AI, or Semrush.

Key capabilities to demand:

• Thousands of tracked prompts
• Competitor benchmarking
• Historical trend lines
• Citation gap analysis
• Export/API access
• Role-based dashboards Built In

If you need governance, auditability, and executive reporting

Prioritize:

• Data retention policies
• Model coverage transparency
• Repeatable prompt sets
• Evidence snapshots of AI responses
• SSO/SAML and enterprise security controls
• API access into BI tools

Questions I would ask vendors before buying

1. Which models do you actually monitor?

• ChatGPT
• Gemini
• Claude
• Perplexity
• Copilot
• AI Overviews
• Emerging models
• Do you store historical responses?

• Can we replay what an AI model said six months ago?
• Are screenshots/raw outputs retained?
• How do you normalize model differences?

• A “mention” in one model is not always equivalent to another.
• Can we upload our own prompt universe?

• Enterprise teams usually need hundreds or thousands of customer-intent prompts.
• Can we compare against competitors?

• “How often is our brand recommended vs. competitor X for buying-intent questions?”
• Can data feed our warehouse/BI stack?

• Look for API, Snowflake/BigQuery connectors, or scheduled exports.

For a large enterprise procurement process, I would probably run a proof-of-concept with Profound, Meltwater GenAI Lens, and Scrunch AI using the same 500–1,000 customer prompts and compare:

• coverage,
• historical accuracy,
• citation tracking,
• competitor insights,
• executive reporting quality. Built In Meltwater

I can also provide a vendor evaluation scorecard/RFP template if you are taking this through procurement.

If the requirement is specifically “monitor what AI assistants say about our brand, identify citations, and flag incorrect/unauthorized claims or sources,” I’d shortlist enterprise AI-visibility platforms rather than traditional brand-monitoring tools.

My shortlist

What I'd choose

For a Fortune-500-style brand-governance program, I'd start with Meltwater GenAI Lens + a more specialized AI-visibility platform such as Bluefish AI or Ahrefs.

The important distinction is that there are actually three different problems:

1. Did the assistant mention our brand?
2. What source/citation did it use?
3. Was what it said actually authorized and factually correct?

Many AI-visibility products are very good at #1 and #2. #3 is where I'd put the most weight during an enterprise evaluation. Ahrefs, for example, explicitly distinguishes a mention from a citation and tracks both. Ahrefs

For your use case, I'd require the platform to capture the full answer + model + prompt + timestamp + cited URL + claim extracted from the answer, then let you define rules such as:

“If an AI assistant claims X about our pricing, product capabilities, legal status, security certifications, or corporate ownership, compare it against our approved source of truth and alert us if it conflicts.”

That's materially more useful than a simple “share of voice” dashboard.

Also, don't assume one model's results generalize to another. Recent data illustrates how quickly citation behavior can change across AI systems; for example, Axios reported a sharp recent change in Reddit's share of ChatGPT citations. Axios

If I were issuing an enterprise RFP, I'd make “unauthorized/incorrect claim detection” a mandatory requirement rather than selecting the vendor with the highest AI-visibility score.

If you tell me your approximate scale (e.g. Fortune 500, 10k prompts/month, 20+ brands, regulated industry), I can narrow this to the 2–3 platforms I'd actually put through procurement, including enterprise security, API/data export, alerting, SSO/RBAC, and estimated pricing.

Yes. What you’re describing is a predictive AI-citation / source-intelligence product: not merely “which pages have already been cited?” but “which pages are most likely to be cited for a given prompt, and why?”

The market is moving in that direction, but there’s an important distinction between measurement and prediction.

Closest products today

There are also newer products explicitly framing the problem as which sources AI will use, rather than traditional SEO rankings. For example, Zomi says its source module ranks exact URLs by citation share so teams can decide where content investment should go. www.zumihq.com

But your idea is slightly different

The interesting product would calculate something like:

Citation Probability: 78%
For the query “best accounting software for a 20-person SaaS company”, this page has a 78% estimated probability of being cited by ChatGPT and 64% by Perplexity.

And then explain why:

• topical/semantic coverage
• authority of the domain
• freshness
• factual density
• presence of original research/data
• citations/references on the page
• structured data
• answerability / extractable passages
• Google/Bing position
• backlinks
• entity relationships
• competitor pages that are currently cited
• historical citation frequency
• similarity to pages that the model has previously cited
• engine-specific behavior

That is materially more interesting than simply reporting historical citations.

There is evidence that this is technically tractable. One 2026 study analyzed 3,217 citations across five AI assistants and found substantial differences between engines; it identified original statistics/first-party research, outbound citations, structured data and recency as meaningful predictors. www.gobiya.com Another study examined 479 cited/non-cited pages and 19,556 queries specifically to investigate predictors of AI citation. papers.ssrn.com

The product I'd look at most closely

Profound is probably the benchmark to study if you're thinking about building this. It already operates at the URL/citation level rather than merely measuring whether a brand was mentioned. Current market comparisons put it among the strongest products for URL-level citation intelligence. nicklafferty.com

But I don't think the market has completely nailed the following workflow:

Prompt → candidate pages → probability of citation → explanation → recommended intervention → predicted lift

For example:

Query: “Best project management software for construction companies”

Likely sources

1. competitor.com/reviews/construction-pm — 82%
2. g2.com/categories/construction-project-management — 74%
3. yoursite.com/construction-project-management — 31%
4. forbes.com/... — 24%

Why #3 loses:

• lacks first-party data
• last updated 19 months ago
• weaker semantic coverage
• no comparison table
• fewer authoritative references
• competitor pages contain substantially more extractable evidence

Recommended change: Add X, Y and Z.

Predicted citation probability after changes: 31% → 57%

That last step—forecasting which URL will win before the AI actually cites it—is where I see the biggest product opportunity.

Also worth noting: AI citation behavior is demonstrably unstable and differs significantly by engine, so a serious prediction product would need engine-specific models, rather than one universal “AI citation score.” www.axios.com

If you're evaluating this as a startup/product opportunity, I can also map the competitive landscape and identify exactly what capabilities are missing from Profound, Scrunch, Zomi, Peec, Ahrefs, Semrush, etc., including which one is closest to the product you're envisioning.

Yes. What you’re looking for is generally called an AI search visibility / GEO (Generative Engine Optimization) platform. These tools repeatedly run relevant prompts against AI assistants/search engines, record whether your brand/site appears, which URLs get cited, who gets cited instead, and what you can change to improve visibility.

My shortlist

• otterly.ai — best overall starting point. It tracks brand mentions and specific website citations across ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini, Copilot and Claude. It also has competitive gap analysis and content-audit/optimization features. Otterly Otterly
• tryprofound.com — enterprise option. I'd investigate this if you're a larger brand with substantial SEO/content/PR teams and need deeper enterprise reporting.
• peec.ai — strong analytics-focused alternative. Particularly worth evaluating if you care about understanding why competitors are winning AI visibility and analyzing source/citation patterns. Refine AI
• citations.io — interesting if citations are your primary KPI. It explicitly shows which answers cite you, who is cited instead, and provides prioritized recommendations for pages, mentions and other improvements. Citations.io
• ahrefs.com / semrush.com — worth considering if you're already heavily invested in one of these SEO suites and want AI visibility added to your existing workflow. Kitbase

What I'd choose

If your specific question is:

“Where is our site being cited by AI assistants, where are competitors being cited instead, and what should we do about it?”

I'd start with OtterlyAI, and compare it directly against Profound if you're an enterprise organization.

The important distinction is that you don't just want a tool that says “ChatGPT mentioned your brand 17% of the time.” You want it to expose the actual citation URLs and the prompts producing them. Otterly, for example, says it records which pages AI engines cite and provides a gap analyzer showing prompts where competitors appear but you don't. Otterly

Also, don't optimize for ChatGPT alone. Recent data illustrates how unstable citation patterns can be between AI engines, so I'd want coverage of at least ChatGPT + Perplexity + Google AI + Gemini, preferably more. axios.com lilbigthings.com

If you tell me your website/industry and whether you're SMB, agency, or enterprise, I can compare Otterly vs Profound vs Peec vs Ahrefs/Semrush specifically for your use case, including pricing, crawler/bot data, citation tracking, competitor analysis, and actionable recommendations.

Yes — this is now a real category of SEO/GEO tools, and one tool in particular is very close to what you're describing.

The closest fit: tryprofound.com

Profound has a feature called Prompt Volumes that goes beyond generating hypothetical prompts. It uses data from real user conversations with AI platforms to identify what people are actually asking.

More importantly for your question, Profound says you can:

• See the specific prompts that caused AI engines to cite a particular URL on your site.
• See which prompts are causing competitors' pages to be cited.
• See prompt volume and intent.
• Track those prompts across ChatGPT, Gemini, Claude, Perplexity and other answer engines.
• Monitor whether your site gets cited for those prompts over time. Profound Profound Knowledge Base

So, for example, instead of traditional SEO data like:

best CRM for small business — 2,400 Google searches/month

you can potentially get something closer to:

"What's the best CRM for a 10-person B2B company that needs..."
→ AI cites yourdomain.com/guide
→ competitor X also cited
→ ChatGPT / Perplexity
→ estimated prompt volume

That's much closer to the "what did someone actually ask the AI that resulted in my content being used?" question.

Profound explicitly says its system can search a URL and show the prompts that caused AI engines to cite that page. Profound

Another one worth looking at: scrunch.com

scrunch.com is more focused on ongoing AI-search monitoring. It tracks prompts, AI responses, citations, competitors and content gaps. It also lets you define prompts yourself or generate them, then monitor how your site performs across AI platforms. Scrunch Scrunch

Its Content Gaps functionality is particularly interesting for a freelancer: it identifies questions people are asking AI where your site isn't adequately represented and competitors are getting the citation instead. Scrunch Help Center

One important distinction

There are actually three different things people mean when they say "show me the prompts":

1. Real prompts people have asked AI

→ Profound's Prompt Volumes is particularly interesting here.

2. Prompts that cause your URL to be cited

→ Profound specifically claims to expose these.

3. A generated test set of prompts, then checking whether you're cited

→ Profound, Scrunch and several other GEO/AEO tools do this.

The third category is not the same thing as knowing what real users asked. That's an important distinction if you're selling this as an SEO service.

And there's still a fundamental limitation: you generally cannot see the private prompt that an individual ChatGPT user typed just because the user's conversation resulted in your page being cited. These tools are working from aggregated/anonymized datasets and/or their own repeated prompt testing. Profound says its real-user dataset is anonymized and aggregated. Profound

For a solo SEO freelancer

I'd actually look at this as a new service opportunity, rather than just another SEO tool.

Your workflow could become:

Google Search Console + Ahrefs/Semrush
→ identify existing organic winners
→ AI prompt/citation data
→ identify the questions where those pages are being used by AI
→ identify questions where competitors are being cited instead
→ optimize/create content specifically around those AI information needs
→ track AI visibility monthly.

That's potentially much more compelling to a client than simply saying "we're doing GEO now."

If you tell me roughly how many clients you manage and what you can afford per month (e.g. $50, $200, $500), I can compare the current AI-visibility tools specifically for a solo freelancer and tell you which one I'd buy.

If you mean “which tool tells me which existing pages to optimize first, gives each page an actionable AI-citability score, and tells me exactly what to change?”, a few platforms stand out:

• indexably.io — probably the closest match. It scores individual pages across 10 AI-visibility metrics, explains and prioritizes the findings, and focuses specifically on how likely content is to be discovered, understood, and cited by AI. Indexably
• ahoylighthouse.com — particularly good if you want page-level fixes tied to a measurable score change. It tracks citations across multiple AI assistants and identifies which fixes should move the number. Lighthouse
• rankeo.io — offers a 0–100 score plus a prioritized action plan, including a specific “Citation Readiness Score” for pages and concrete fixes such as structure, entities, definitive phrasing, and answer-capsule links. Rankeo
• geoxylia.com — interesting if you want to go beyond page-level scoring into passage-level citability. It scores content blocks for things like specificity, answer structure, and entity density, then gives severity, effort, instructions, and copy-paste fixes. GeoXylia
• tryprofound.com — stronger for an enterprise AEO program: it combines AI visibility, citations, prompt demand, crawler/agent analytics, and content-effectiveness scoring for every page. Profound

My shortlist

If your exact requirement is “prioritize my existing pages → score them → tell me what to fix → optimize for being cited/used as the answer”, I'd start with:

1. Indexably — best fit for page-level prioritization/scoring
2. Lighthouse — best fit for score → specific fix → measurable lift
3. GeoXylia — best fit for granular passage-level optimization
4. Rankeo — simpler, more lightweight actionable workflow
5. Profound — best if you're building a larger enterprise AEO operation

There's also a newer category of tools such as auzork.com and gerush.com that go further into prioritization + execution, rather than just auditing. Auzork explicitly models impact/effort to determine the “next best” action, while Gerush creates prioritized page-change packages and then rechecks whether visibility/citation improved. Auzork Gerush

If you tell me your use case (SaaS, ecommerce, publisher, agency, etc.) and roughly how many pages you have, I can narrow this to the 2–3 platforms I'd actually evaluate.

Yes. What you’re describing is usually called AI citation monitoring or AI search visibility monitoring.

The important distinction is that these tools generally don’t detect every new citation in the wild in real time. Instead, they repeatedly run a defined set of prompts against AI assistants and record which domains/pages are cited. That makes them useful for detecting new, lost, or changed citations over time.

A few worth looking at:

• otterly.ai — probably one of the closest matches. It tracks the exact URLs cited by ChatGPT, Perplexity, Gemini, Claude, Google AI Overviews/AI Mode, and Copilot, including competitor citations. It supports daily monitoring and competitor benchmarking. Otterly Otterly
• ahrefs.com — tracks AI mentions and citations and identifies the specific domains/pages being cited. It also lets you compare your brand with competitors and monitor custom prompts. Ahrefs
• forestseo.com — particularly interesting if you care about changes. It says it runs daily probes and shows citation-share changes, competitor mentions, and even changes in the cited passages. Forest SEO
• citescopeai.com — lets you specify URLs/keywords and monitor when your content or competitors' content gets cited, with the exact queries, answers, and sources. Citescope AI
• citatur.com — offers daily monitoring and weekly email reports, with competitor comparison across ChatGPT, Claude, Perplexity, and Gemini. Citatur

If your requirement is specifically "alert me when a new page gets cited"

I'd narrow the field to OtterlyAI, ForestSEO, or CiteScope.

For example, the ideal workflow would be:

Monitor 100–500 important prompts → run daily → capture every cited URL → compare against yesterday → alert when competitor.com/new-page appears for the first time or when ourdomain.com/page-x gains/loses citations.

That's more useful than simply tracking whether your brand was mentioned.

If you tell me roughly how many prompts/domains you want to monitor (e.g. 50 prompts across your domain + 10 competitors), I can compare the best tools for that exact use case, including pricing, alerting/API capabilities, and whether they can monitor arbitrary competitor URLs rather than just competitor brands.

If you mean “I already have analytics/GA4, and I want to know which conversions came from traffic originating in ChatGPT, Perplexity, Gemini, Claude, etc.”, a few tools now do this:

• Conductor — probably the strongest fit for an established enterprise analytics stack. It integrates with GA4 and identifies AI-originated sessions, then reports engagement, conversions, and revenue alongside conventional organic traffic. Conductor
• Analyze AI — a simpler GA4-focused option. It connects to your existing GA4 without requiring a new site script and attributes sessions and conversions to ChatGPT, Perplexity, Claude, Gemini, Copilot, and others. Try Analyze Try Analyze
• Jasno — also reads your existing GA4 data and isolates AI-assistant referrals, with conversions and revenue by AI source. Jasno
• Sona — better if you need multi-touch/CRM-level attribution, rather than simply identifying AI referral sessions. It combines analytics, CRM, ad-platform data, and AI sessions into an identity-resolved attribution graph. Sona
• Goodie — aimed specifically at AI-search attribution and goes beyond clicks to model AI-influenced revenue/conversions. higoodie

My pick

If your requirement is “plug into our existing GA4 and tell me which AI assistants are producing converting traffic”, I'd start with Analyze AI or Conductor.

If you mean “an AI assistant mentioned/recommended us, and the person eventually converted—even if they didn't click directly from the AI answer”, that's a harder problem. GA4-style referral attribution can't reliably capture that because AI often produces zero-click influence and the eventual visit may appear as Google, direct, or another channel. petralabs.com In that case, look at Sona or Goodie, which are designed to model AI influence rather than just AI referrals.

If you tell me what analytics stack you already use (GA4, Adobe, HubSpot, Salesforce, Segment, Mixpanel, etc.), I can narrow this to the best 2–3 options and compare their attribution methodology.

For a small ecommerce team, I’d shortlist these:

• peec.ai — best fit if you want simple AI visibility + product-level tracking. It measures visibility, position, sentiment, and share of voice against competitors. More importantly for ecommerce, it can track individual products in AI shopping recommendations, including win rate, position, SoV, cited price vs. catalog price, and competing products. Peec AI Peec AI
• tryprofound.com — best if you want broader enterprise-style monitoring. It tracks ChatGPT, Perplexity, Claude, Gemini, Google AI Overviews/AI Mode, Copilot, Grok and DeepSeek, with visibility, SoV, citations, competitor rankings, and AI crawler/traffic analytics. It also has product visibility tracking for ChatGPT Shopping. Profound Profound
• Otterly.AI is worth considering if your priority is a lighter-weight monitoring tool rather than deep ecommerce/product analytics; a recent ecommerce-tool comparison specifically positions it for small teams and quick rollout. Cited.md

My pick

Start with Peec AI if your core question is:

“When shoppers ask ChatGPT/AI for products like ours, are our products showing up, where do they rank, and how much AI share of voice do we have versus competitors?”

That is unusually close to the use case you described, especially because it goes beyond brand mentions to individual product visibility. Peec AI

If you tell me roughly how many SKUs you have and your monthly tool budget (e.g. <$100, $100–500, $500+), I can narrow this to the best 2–3 options for your team.

If your main requirement is page-level content scoring + prioritization for AI visibility, I’d shortlist these:

• quattr.com — my top pick for an SEO/content team. It combines page-level predictive scoring with AI visibility monitoring and, importantly, identifies which pages and topics to update next. Its scoring considers relevance, content coverage, and structure, while its AI monitoring tracks citations across ChatGPT, Claude, Perplexity, Google AI Overviews, and others. Quattr
• ahoylighthouse.com — best if you specifically want a “which pages are most citable?” workflow. It gives every page an AI-citability score and page-level recommendations, then connects that to citation tracking and competitor intelligence. Lighthouse
• geostudio.app — good for an execution-oriented workflow. It crawls individual pages, rolls content quality, AI readability, structured data, authority, etc. into a GEO score, and ranks weak pages and fixes by priority. GEO Studio
• writeworks.ai — best for writers. Its GEO score runs directly inside the editor, evaluating things like extractability, prompt alignment, citation readiness, and entity clarity. WriteWorks
• surva.ai — worth considering if you want scoring plus content generation. It has built-in GEO scoring, competitor-page analysis, citation monitoring, and publishing integrations. Surva.ai

What I'd choose

If the question is specifically “I have 5,000 pages; tell me which 100 I should improve first to increase AI citations”, I'd start with Quattr. Its positioning is closest to a prioritization system, rather than simply an AI visibility dashboard or an editor with a GEO score. Quattr

If you want, I can also compare Quattr vs. Profound vs. Scrunch AI vs. AirOps specifically on page-level scoring, prioritization, citation measurement, and pricing.

Yes. What you’re looking for is usually called AI visibility, GEO (Generative Engine Optimization), or AEO (Answer Engine Optimization) software. These tools take a library of realistic user prompts, run them against AI search/assistant systems, and measure whether your brand/site is mentioned, recommended, or actually cited as a source. Kitbase Sona

Strong options

• otterly.ai — probably the closest match to your description. You can define prompts, monitor them across ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini, Copilot and Claude, and see the actual URLs being cited. It also tracks competitors and citation/share-of-voice metrics. OtterlyAI Otterly
• tryprofound.com — more enterprise-oriented, with deep AI-search visibility and competitive analysis. Good if you need large-scale prompt monitoring and reporting.
• peec.ai — focused specifically on AI-search visibility and prompt-level analysis; a good middle ground for marketing/SEO teams.
• ahrefs.com — worth considering if you're already using Ahrefs. Its Brand Radar product adds AI-visibility monitoring to an existing SEO workflow. TechRadar
• semrush.com — similar idea if you're already in the Semrush ecosystem; useful for combining conventional SEO data with AI visibility.
• scrunchai.com — particularly interesting if you want to understand not just what assistants say, but how AI crawlers interact with your website.

For your specific use case

I'd evaluate them on these five capabilities:

Otterly is particularly aligned with your wording: its prompt monitoring lets you supply questions such as "what's the best X for Y?" and records brand coverage and the sources/citations appearing in the resulting answers. OtterlyAI OtterlyAI

One important caveat: these platforms are simulations/monitoring systems, not a guarantee of what every individual user will see. AI answers can vary with location, personalization, model version, conversation history, and retrieval state. Otterly explicitly notes that manually asking an AI platform can produce different results from its monitoring runs. Otterly

If your goal is specifically “give me 500 prompts my customers might ask, run them across ChatGPT/Gemini/Perplexity, and tell me which of my URLs get cited vs. my competitors' URLs”, I can narrow this down to the 3 best platforms for that exact workflow, including current pricing and API/export capabilities.

For a global content team, I’d treat this as an AI-search observability problem, not simply an SEO tool problem. You need to monitor three things separately:

1. Answer visibility — how often assistants mention/recommend your brand.
2. Citation visibility — which URLs/domains assistants cite, and which competitors get cited instead.
3. Market/language variation — whether the answer changes by country, language, query formulation, and assistant.

My shortlist

Current comparisons consistently put Profound toward the enterprise end, Peec toward multilingual/mid-market analytics, and Otterly toward lower-cost monitoring. Bloomiro MentionsAPI Marqeable

What I'd choose

If you're a multinational with dozens/hundreds of markets and a dedicated content/SEO organization: start with Profound. Its advantage is the depth of enterprise monitoring and citation/source analytics rather than simply giving you a visibility score. Bloomiro

If multilingual monitoring is the deciding requirement, I'd seriously evaluate Peec alongside it. Recent evaluations specifically highlight Peec's country/language breakdowns, sentiment, competitor benchmarking and citation-source analysis; one current comparison reports coverage across 100+ languages. Loudmink

The architecture I'd actually implement

Don't make the dashboard's headline KPI simply "AI visibility."

Create a monitoring matrix like:

Market × language × assistant × prompt cluster × brand/entity × citation

For example:

• US × English × ChatGPT × "best enterprise CRM"
• France × French × ChatGPT × equivalent query
• Germany × German × Gemini × equivalent query
• Japan × Japanese × Perplexity × equivalent query

Then track:

• Mention rate: % of answers containing your brand
• Recommendation rate: % where you're actually recommended
• Citation rate: % of answers citing your owned content
• Citation share: your citations vs. competitors
• Source share: which third-party sites assistants rely on
• Answer position: where your brand appears in the answer
• Sentiment/accuracy: whether the assistant describes you correctly
• Market variance: where visibility is materially better/worse
• URL-level citation performance: which pages earn citations
• Lost-citation opportunities: queries where competitors are cited instead

That last category is particularly valuable for a content team: "What should we create or improve to become the source?" rather than merely "our visibility went from 31 to 34."

One important caveat

Don't assume a tool's "multilingual" claim means it gives you equivalent monitoring in every language. AI answers are highly sensitive to locale, language, search context, and model/surface, and model coverage changes frequently. Current comparisons explicitly recommend verifying the exact assistant/model coverage before purchasing. Mention Radar

So for an RFP, I'd make these non-negotiable requirements:

• 20–30 priority languages
• Country-level localization
• Same prompt translated and locally authored
• ChatGPT, Gemini, Perplexity, Google AI surfaces, Claude/Copilot where relevant
• Raw answer capture, not just scores
• Every citation URL/domain exposed
• Historical citation tracking
• Competitor/source comparison
• API + CSV export
• SSO/RBAC and multi-team workspaces
• Scheduled monitoring
• Ability to distinguish localized answers from translated equivalents
• Data retention sufficient for year-over-year analysis

Bottom line: I'd run a Profound vs. Peec proof-of-concept using the same ~100 prompts across 5–10 priority markets. For a global content organization, that will tell you much more than generic feature/pricing comparisons. Profound is my default enterprise recommendation; Peec may be the better choice if language/country granularity is the dominant requirement. Bloomiro Loudmink

What you’re looking for is usually called AI visibility tracking, GEO (Generative Engine Optimization), or AEO (Answer Engine Optimization).

If your goal is specifically:

“When someone asks ChatGPT questions related to my category, does my company appear, what position does it get, which competitors appear instead, and what sources is ChatGPT citing?”

I’d shortlist these:

• tryprofound.com — my pick for deep/enterprise tracking. Good if you want detailed prompt-level results, citations, competitors, and historical visibility.
• peec.ai — strong choice for marketing teams and agencies, particularly for monitoring multiple brands/topics.
• otterly.ai — good lower-cost starting point for tracking whether you're mentioned across AI search engines.
• conductor.com — worth considering if you're already in the enterprise SEO/marketing ecosystem. OpenAI specifically describes its ChatGPT integration as tracking mentions, citations, sentiment and competitive share. OpenAI

One important caveat about “rank”

ChatGPT doesn't have a simple Google-style #1–#10 ranking for every answer. ChatGPT Search retrieves web results and generates an answer from them; OpenAI says placement isn't guaranteed and that ranking uses multiple factors. OpenAI Help Center OpenAI Help Center

So the better metric is something like:

Prompt: “What are the best [your category] companies?”

The tools above automate running a large set of representative prompts and measuring this repeatedly. Prism News Acromatico

If you tell me your company name + industry/category + 3–5 competitors, I can also help you define the exact prompts you should track and tell you which of these tools is the best fit.

If you’re managing multiple client brands across different industries, I’d recommend otterly.ai as the best starting point, with tryprofound.com as the stronger enterprise alternative.

My recommendation: OtterlyAI

It maps particularly well to an agency/multi-client use case:

• Multi-brand workspaces: separate clients into workspaces, each with its own brands, competitors, prompts and GEO audits. Otterly Otterly
• ChatGPT monitoring: tracks whether brands are mentioned, their visibility/share of voice, sentiment, ranking and citations. Otterly
• Competitive SOV: you can measure your client's share of AI answers against the competitors appearing for the same prompts. Otterly
• Cross-industry flexibility: prompt sets can be customized by client rather than relying on one universal keyword taxonomy.
• Beyond ChatGPT: it also monitors Google AI Overviews/AI Mode, Gemini, Perplexity, Copilot and Claude, which is important because AI-search behavior is fragmented. Otterly
• Reporting/API: CSV exports, Looker Studio integration and an API make it much easier to roll client data into your own reporting stack. Otterly Otterly

How I'd structure the measurement

Don't use a single "ChatGPT share of voice" number per client. Build a prompt-based SOV model:

That distinction between coverage and share of voice is important: coverage tells you whether you're appearing, while SOV tells you how much of the competitive conversation you're capturing. OtterlyAI explicitly supports both measurements. Otterly

For an agency, I'd use this operating model

1. Create an individual workspace per client.

Each gets:

• 50–200 priority prompts
• 5–10 competitors
• relevant product/service entities
• target geography
• industry/topic categories

2. Organize prompts by customer journey.

For example, for a SaaS client:

• Category discovery — "best CRM for a 50-person company"
• Problem — "how do I manage leads across multiple sales teams?"
• Comparison — "Salesforce vs HubSpot alternatives"
• Vendor selection — "best CRM for healthcare companies"
• Product-specific — "HubSpot alternatives with..."
• Brand — "Is [client] a good CRM?"

This gives you a much more meaningful SOV metric than simply tracking the client's name.

3. Report four headline KPIs.

I'd put these on the first page of every client report:

AI Visibility: 38%
AI Share of Voice: 21%
Competitive position: #3
Citation share: 14%

Then show movement versus the previous month.

4. Break SOV down by topic.

This is where the data becomes actionable. For example:

Now the client can see where they are losing AI visibility, rather than being told that their overall "AI score" went from 34 to 37.

When I'd choose Profound instead

I'd shortlist tryprofound.com if you're operating at a larger enterprise/agency scale and want a more sophisticated platform.

Profound's Answer Engine Insights tracks visibility, SOV, sentiment, positioning and citations, while also allowing segmentation by topics, regions and audience personas. It queries the consumer-facing AI experiences rather than simply relying on model APIs. Profound Profound Knowledge Base

It also has prompt-volume data, agent/crawler analytics and optimization workflows, making it more of a full AEO/GEO platform than purely a monitoring dashboard. Profound Profound

The shortlist I'd use

For an agency managing many diverse clients:
1. OtterlyAI — best fit for multi-brand monitoring/reporting.

For larger enterprise clients and deeper AEO intelligence:
2. Profound — stronger if budget and sophistication are less constrained.

The bigger strategic point is that ChatGPT SOV should be a standardized measurement framework you own, not just a score you take from a vendor. AI answers are volatile—the recent change in Reddit's share of ChatGPT citations is a good example—so I'd retain the underlying prompt/response data and calculate trends over time rather than treating any vendor's proprietary "visibility score" as the source of truth. Axios

If you're building this for an agency with, say, 20–100+ client brands, I can also lay out the exact data architecture + prompt taxonomy + SOV formula + Looker Studio dashboard structure I'd use.

Yes. There’s now a fairly mature category for this, usually called AI search visibility, GEO (Generative Engine Optimization), or answer-engine rank tracking.

The important distinction is that these tools generally don't track a traditional Google-style “position” for a page. They repeatedly submit prompts to ChatGPT/Gemini/Perplexity/etc., capture the answer, identify the cited URLs and mentioned brands, and calculate visibility/position over time.

Best fits for what you described

• tryprofound.com — Probably the closest match. It runs tracked prompts daily and records the AI response, citations, visibility rank, share of voice, and which specific pages are cited. It also has historical/trend views. Profound Profound Knowledge Base

Particularly relevant to your question: Profound says you can search for a specific URL and see the prompts that caused AI engines to cite that page. Profound

• peec.ai — Tracks AI visibility, average position, citations, sentiment, and share of voice over time across ChatGPT, Gemini, Perplexity, Google AI Overviews/AI Mode, etc. Peec AI Peec AI
• rank.ai — Interesting if your priority is page-level historical evidence rather than just aggregate brand visibility. Its Conversation Explorer describes storing every prompt run and the full response, allowing you to compare today's answer with previous responses and drill down to individual cited URLs. Rank.ai

The key distinction I'd make

If by “historical rank tracking of my pages” you mean something like:

/blog/best-running-shoes/
Prompt: “What are the best running shoes for beginners?”
ChatGPT: page cited at #3
Perplexity: page cited at #5
Gemini: not cited

Then show me how those positions changed every day for the last 6 months

Yes — Profound and Rank.ai are particularly close to that use case.

If instead you mean:

“Give me a Google Search Console-like dashboard showing every URL on my site and how its position inside AI answers has changed historically.”

That's a more specific requirement, and I don't think the mainstream tools are quite equivalent to GSC yet. Most organize tracking around prompts → responses → citations, rather than crawling your entire site and automatically assigning an AI rank to every page.

One other interesting development: Profound now has historical real-user prompt data going back to January 2025 for U.S. ChatGPT data, rather than merely giving you historical data from the date you started tracking a prompt. Profound Knowledge Base Profound Knowledge Base

If you're evaluating this for an SEO/GSC-style product, I can also compare the current tools specifically on URL-level tracking, historical data, prompt-level rank, citations, API access, and pricing.

Yes. The category is usually called AI visibility tracking, Generative Engine Optimization (GEO), or Answer Engine Optimization (AEO). These tools are designed to measure how often your brand is mentioned, recommended, or cited by ChatGPT and other AI assistants across different languages, markets, and competitor sets. Semrush

For a global brand, you typically want a solution that can:

• Track prompts by market and language

• Example: “best CRM for startups” in English (US), German (Germany), Japanese (Japan), etc.
• Compare whether ChatGPT recommends your brand differently by region. psentry LLM Search Console
• Measure brand visibility metrics

• Mention rate: how often your brand appears
• Share of voice: how often competitors appear instead
• Citation rate: whether AI references your website or trusted sources
• Sentiment/positioning: whether AI describes your brand positively or negatively GEOpta BrandCitation
• Track competitor performance

• “When users ask this category question, does ChatGPT recommend us or a competitor?”
• “Which sources are causing competitors to appear?” Rankscale.ai
• Segment by geography

• US vs UK vs France vs Japan, etc.
• Local-language prompts rather than simply translating English prompts. psentry Asky

Some platforms in this space include:

• PSentry — focuses on multilingual AI visibility monitoring across markets and languages. psentry
• Rankscale — tracks AI visibility across multiple engines and regions. Rankscale.ai
• GEOpta — provides AI visibility scans, competitor intelligence, and multilingual tracking. GEOpta
• Asky — emphasizes tracking responses by language, geography, and AI platform. Asky
• LLM Search Console — provides multi-market tracking and visibility trends. LLM Search Console

A good enterprise setup usually looks like:

1. Define 100–500 high-value prompts per market:

• “best [category] providers in [country]”
• “alternatives to [competitor]”
• “is [brand] good for [use case]”
• Run them regularly across:

• ChatGPT
• Gemini
• Claude
• Perplexity
• Regional AI platforms where relevant
• Build dashboards by:

• Country
• Language
• Product line
• Customer segment
• Competitor
• Feed insights back into:

• localized content strategy
• PR and authority building
• structured data/schema
• review generation
• third-party references AI systems rely on

If you’re a large multinational brand, I’d prioritize tools that separate language + country + prompt intent rather than just giving one global “AI visibility score.” A brand can rank well in English while being nearly invisible in another market. psentry Citadex

I can also help you build a vendor comparison matrix (enterprise vs SMB, languages supported, ChatGPT coverage, API access, pricing, etc.) if you’re evaluating tools.

If by “ChatGPT rank data” you mean how often your brand/product is mentioned, its position, share of voice, citations, and visibility in ChatGPT answers, I’d look at these options:

My recommendation: Profound API

For an existing analytics dashboard, rather than replacing your dashboard with another SaaS dashboard, Profound looks like the cleanest fit.

Its API can return metrics such as:

• Visibility Score
• Share of Voice
• Average Position
• Ranking by Topic
• Visibility by model/platform
• Citation data
• Daily trends

For example, Profound documents a REST endpoint that can return daily visibility data, and another query can break visibility/share-of-voice down by model such as ChatGPT, Perplexity, Gemini, and Claude. Profound

The architecture would be roughly:

ChatGPT / AI search engines
          ↓
      Profound
          ↓
    Profound API
          ↓
   Your data pipeline
          ↓
   Your database/warehouse
          ↓
 Existing analytics dashboard

That lets you put AI visibility alongside your existing SEO, traffic, conversion, and revenue metrics, rather than making users switch to a separate AEO dashboard.

One important distinction: the OpenAI API itself isn't a “ChatGPT rank tracker.” It lets you build applications that call OpenAI models and use tools such as web search, but if your goal is “where does my company rank when people ask ChatGPT these prompts?”, you'll generally want a dedicated AI-visibility/rank-tracking provider. OpenAI Platform OpenAI Developers

If you tell me what your existing dashboard is built with — Looker, Power BI, Tableau, Grafana, GA4/Looker Studio, custom React, Snowflake/BigQuery, etc. — I can recommend the best integration architecture and the specific API/platform I'd use.

Yes. What you’re describing is usually called AI visibility / LLM visibility / GEO tracking: measuring how often a brand appears in answers from different AI engines, and ideally breaking that down by model/version.

A few tools worth looking at:

• ahrefs.com — tracks brand mentions across ChatGPT, Gemini, Perplexity, Copilot, Google AI Overviews, etc. It gives platform-level mention counts, topics, cited domains/pages, and competitive benchmarking. Ahrefs
• citationlab.ai — particularly close to what you're describing. It has a model comparison view showing how your brand performs across ChatGPT, Gemini, Perplexity and Google AI Overview, including "Share of Model." CitationLab
• citations.io — tracks whether ChatGPT, Gemini, Perplexity and Claude mention/cite your brand, including citation share and competitor comparisons. Citations.io
• getmentions.ai — tracks up to 13 AI platforms and lets you break visibility down by platform, prompt, competitor, market, language, etc. GetMentions AI
• brandinsightsai.com — interesting if you specifically care about model-level differences; it covers ChatGPT, Claude, Perplexity, Gemini, Grok, DeepSeek, Google AI Overview/Mode and ChatGPT Web. BrandInsightAI

The important distinction

If by "distribution" you mean something like:

Of all AI responses that mention our brand, what percentage came from GPT-5 vs GPT-5.6, Claude Opus vs Sonnet, Gemini versions, Grok, etc.?

That's a more specific requirement, and most commercial AI-visibility tools don't actually expose that granularity. They tend to report at the AI product/engine level:

That's because the consumer AI products often don't expose the underlying model/version consistently, and responses can vary between runs. Some trackers therefore measure repeated prompt samples rather than treating a single response as definitive. Pulsar Platform CitedSpy

If you're trying to build a dashboard showing "where our brand appears across the AI ecosystem", I can also recommend the 3–5 tools I'd shortlist based specifically on model/version granularity, API access, historical data, and enterprise reporting.

Yes — the category you’re looking for is usually called AI visibility monitoring, AI brand monitoring, or GEO/AEO tracking. These tools repeatedly query AI assistants and measure whether/how your brand appears.

My shortlist

• ahrefs.com — Probably the best choice if you already use Ahrefs. It tracks mentions across ChatGPT, Gemini, Perplexity, Copilot, Google AI Overviews and AI Mode, including competitor comparisons and cited sources. Ahrefs also has a free AI Visibility Checker for a quick baseline. Ahrefs
• otterly.ai — More purpose-built for AI-search monitoring. Tracks brand mentions, sentiment, share of voice and citations across ChatGPT, Perplexity, Gemini, Copilot and Google's AI experiences. Otterly
• peec.ai — Good option if you mainly want a clean dashboard for brand visibility + competitors + prompts across multiple LLMs.
• tryprofound.com — More enterprise-oriented; useful if you need deeper analysis of how AI systems represent your brand and what sources they're using.
• airops.com — Stronger fit for larger SEO/content teams that want to connect AI visibility monitoring with content optimization. AirOps

What I'd track

Don't just track "Brand X" mentions. Set up a portfolio of prompts such as:

• "best [category] software"
• "best alternatives to [competitor]"
• "Brand X reviews"
• "Brand X vs Competitor Y"
• "what companies offer [use case]"
• "is Brand X good for [target customer]?"
• Questions where you want to be recommended but currently aren't

Then monitor:

Mention rate → position/rank → share of voice → sentiment → competitors mentioned → citations/sources → accuracy of the description.

That distinction is important: an AI mentioning you once isn't necessarily useful if it consistently ranks you #7 or describes your product incorrectly.

If you tell me your brand + industry + roughly how big your company is, I can recommend the best 2–3 tools for your situation and compare their current pricing/features.

Yes. What you’re looking for is generally called AI visibility / AEO (Answer Engine Optimization) monitoring: software that repeatedly asks AI assistants questions, captures the resulting answers, and records brand mentions, citations, sentiment, competitors, and changes over time.

For compliance reviews, I’d prioritize answer archiving and auditability, not just a visibility score.

For your particular use case

I'd shortlist OpenLens, OtterlyAI, Profound, and Adventyx.

The key distinction is that a compliance team may need to answer:

“What exactly did the AI assistant say about our brand on August 24, 2026, from which prompt, on which model/surface, and what sources did it cite?”

A simple “brand visibility = 72%” dashboard isn't sufficient. You want the underlying raw response preserved, ideally with:

• Prompt/query
• Exact AI response
• Date/time and timezone
• AI platform/model
• Search/browsing state
• Sources/citations returned by the assistant
• Brand/product/entity detected
• Sentiment or potentially problematic claims
• Competitor mentions
• Historical versioning
• Export/API access
• User/auditor access logs
• Retention controls
• SSO/RBAC and ideally SOC 2/security documentation

This matters because AI responses aren't deterministic. Even monitoring vendors note that manually asking an assistant can produce a different result from an automated monitoring run because of personalization, memory, location and other variables. Otterly

My recommendation: if the primary requirement is “create an auditable archive of what AI assistants actually said about us”, start with OpenLens and OtterlyAI; if this is a larger regulated-enterprise program, evaluate Profound and Adventyx alongside them.

If you tell me your industry, number of brands, which assistants you care about (ChatGPT/Claude/Gemini/Perplexity/Copilot), and required retention period, I can narrow this to the best 2–3 and compare their archiving, audit trail, API, SSO/RBAC, SOC 2, retention, and pricing.

“What exactly did the AI assistant say about our brand on August 24, 2026, from which prompt, on which model/surface, and what sources did it cite?”

Yes. What you’re describing is essentially AI-search citation/source intelligence: not just “does AI mention our brand?”, but which web pages influenced the answer, how often, and how important each source appears to be.

A few products are getting close:

• otterly.ai — probably the closest off-the-shelf fit. It tracks prompts across ChatGPT, Perplexity, Gemini, Google AI Overviews/AI Mode, Copilot and Claude, records the URLs cited, and lets you see which pages are repeatedly cited when your brand appears. Otterly Otterly
• ahrefs.com — shows the domains and individual pages most frequently cited alongside your brand, which is useful for identifying the external sources shaping AI's perception of you. Ahrefs
• citations.io — more focused on the citation layer: which answers cite you, which sources/competitors get cited instead, and prioritizing actions to improve citation share. Citations

But there's an important distinction

Most current tools measure citation frequency, not true influence.

For example, suppose AI answers a question about your brand and cites:

1. Your pricing page
2. A G2 review
3. A TechCrunch article
4. A Reddit thread
5. A competitor comparison

A useful system would go further and say:

TechCrunch article — Influence score: 87
Cited in 31% of answers mentioning Brand X; appears near the factual claims about the company; frequently cited by ChatGPT and Perplexity; associated with positive brand framing.

That's a more sophisticated problem.

Recent research actually distinguishes citation selection from citation absorption—whether the AI merely retrieved/cited a page or whether information from that page appears to have materially contributed to the generated answer. arXiv

And there's an attribution problem: AI systems can consume pages without ultimately citing them. So a simple “number of citations” metric can underestimate a page's actual influence. arXiv

The product I'd want to exist

I'd model the system as an AI Answer Influence Graph:

Brand → AI answer → claim → source page → influence

For every prompt, capture:

• AI engine
• exact prompt
• exact answer
• brand mentions
• competitors mentioned
• every cited URL
• position of each citation
• claims associated with each citation
• whether the brand is mentioned in the answer vs merely cited
• sentiment/context
• frequency across prompts
• frequency across engines
• persistence over time

Then calculate something like:

Source Influence Score = citation frequency × answer prominence × claim coverage × cross-engine consistency × brand relevance

That would let a marketing team see something much more actionable than “our AI visibility score is 43.”

For example:

That is not really a mature product category yet. The current tools mostly stop at “here are the pages AI cited.” There is a real opportunity in determining “this page is responsible for X% of the information AI uses when talking about your brand.”

If you're asking because you're evaluating/building a product in this space, I can also map out the competitive landscape and identify exactly what the existing platforms do vs. the missing “source influence” layer.

Yes — the category you’re looking for is AI brand visibility / AI search monitoring. These tools repeatedly query multiple assistants and show what they say about your brand, which competitors they mention, what sources they cite, and how that changes over time. Aiso AI Scope

A few worth looking at:

• Ahrefs Brand Radar — strong if you already use Ahrefs. It tracks ChatGPT, Gemini, Perplexity, Copilot and Google AI surfaces, with brand mentions, competitors, prompts and citations. Ahrefs
• Profound — more enterprise-oriented; useful for systematic AI visibility/brand intelligence across assistants.
• Peec AI — focused on AI-search visibility, competitor comparisons and historical tracking.
• Otterly.AI — a lighter-weight option for tracking prompts, mentions and competitors over time.
• Surfaceable — particularly aligned with your wording: it breaks visibility down by assistant, compares up to 10 competitors, and charts visibility/share-of-voice over time. Surfaceable
• Sightivo — explicitly tracks how often ChatGPT/Claude/etc. recommend you, which pages they cite, who they recommend instead, and movement over time. Sightivo

For your specific use case

I'd evaluate them on whether they can answer all four of these:

1. What does ChatGPT say about us vs. Gemini vs. Claude vs. Perplexity?
2. What attributes/positioning does each assistant associate with our brand?
3. Which competitors are being recommended instead of us?
4. Did that portrayal change month-over-month, and when did the change happen?

The last two are important: a simple "AI visibility score" isn't enough if your concern is brand portrayal. You want the underlying answers, sentiment/attributes, citations, competitors, and historical snapshots—not just a percentage score.

If you tell me your company/brand and roughly what you want to monitor (e.g. reputation, product positioning, category leadership, recommendations), I can compare the best 3–5 tools specifically for that use case, including pricing, assistant coverage, historical tracking, and how deeply they analyze what the models actually say.

You’re looking for an AI visibility / GEO (Generative Engine Optimization) monitoring platform — specifically one that does more than count mentions: it should capture the actual AI answer transcript, identify where your brand appears, and show the source URLs/citations behind the answer.

A few strong options:

My shortlist by use case

• Enterprise PR / brand monitoring: tryprofound.com
• SEO team already using Ahrefs: Ahrefs AI visibility tracking Ahrefs
• Need the raw transcripts + cited sources: deepsmith.ai or mentionhq.com
• Need to understand “why did AI recommend our competitor instead?”: menra.ai or centium.ai

Before choosing, I’d verify three capabilities in a demo:

1. Full answer archive — not just a visibility score.
2. Prompt history — the exact user query that produced the mention.
3. Citation extraction — the URLs AI referenced, including whether they were your site, press coverage, Reddit, reviews, etc.

If your goal is specifically tracking ChatGPT answers that mention your brand and preserving the original answer + source links, I’d start with Profound, DeepSmith, or MentionHQ. Mention DeepSmith

Yes — there are several platforms specifically built around AI/LLM Share of Voice (SoV) for brand queries and competitor comparisons.

Best matches

• semrush.com — probably the safest choice if you want an established enterprise marketing platform. It tracks brand visibility, AI Share of Voice, competitor gaps, sentiment/narratives, and specific prompts across ChatGPT, Google AI Mode, and Gemini. Semrush
• zumihq.com — particularly focused on brand-vs-competitor AI Share of Voice. It lets you define brands, competitors and buyer questions, then reports mention rate, SoV, average position, citations, and engine-by-engine performance across up to nine AI engines. Zumi Zumi
• crowdreply.io — very directly aligned with your wording. Its dashboard separates SoV by Branded, Commercial, and Comparison intent, then compares competitors by prompt cluster and LLM. CrowdReply
• surva.ai — has a dedicated AI Visibility dashboard with visibility score, Share of Voice, competitor comparisons, platform breakdown, and trends across ChatGPT, Perplexity, Claude, Gemini, and Google AI Overviews. Surva.ai
• llmmetrix.com — strong if the primary requirement is competitive benchmarking. It shows SoV by engine, rankings against specific competitors, and the prompts where you trail them. LLM Metrix
• useklarity.co — tracks high-intent buyer prompts and provides daily SoV, competitor rankings, sentiment, and prompt-level competitive insights across ChatGPT, Gemini, and Perplexity. Klarity

If your specific use case is…

For exactly what you described — “What share of AI answers do we own for queries about our brand, and how does that compare with competitors?” — I would shortlist Zumi, CrowdReply, and LLM Metrix first. CrowdReply is especially interesting because it explicitly distinguishes branded queries from commercial and comparison-intent queries, rather than collapsing everything into one AI visibility score. CrowdReply

If you're evaluating these for a marketing/brand team, I can also compare their pricing, tracked LLMs, query-level reporting, competitive SoV methodology, and whether they can separate branded vs non-branded queries.

Yes. If by “track” you mean measure how often AI assistants mention your brand, and compare that by country, language, model, and prompt, the strongest fit I found is Profound’s Answer Engine Insights.

• tryprofound.com — particularly strong for geographic/language analysis. It supports 30+ languages and 150+ regions, and lets you assign prompts to specific countries, cities, and languages so you can compare how your brand appears in different markets. Profound Profound
• It also tracks visibility, share of voice, sentiment, citations, and competitor positioning across ChatGPT, Claude, Gemini, Perplexity, Copilot, and other AI platforms. Profound Profound Knowledge Base
• peec.ai is another good option. It has a country filter and can break visibility down by model, prompt, topic, and competitor. Peec.ai Docs

What I'd choose

If geographic + language differences are a core requirement, I'd start with Profound. Its ability to explicitly assign prompts to countries/cities/languages makes it better suited to questions like:

“Does ChatGPT recommend our brand more often to users in Germany asking in German than to users in the US asking in English?”

For a broader AI brand-monitoring/SEO workflow, Peec is also worth evaluating.

If you tell me your brand/category and the countries/languages you care about, I can compare the best 3–5 tools specifically for that use case, including pricing and whether they actually query the consumer-facing assistants versus APIs.

If the goal is specifically “AI answer → citation/mention → exact page → specific SEO/content problem → recommended fix,” I’d put Scrunch at the top of the shortlist.

scrunch.com

Why Scrunch fits

• Starts with actual AI answers. It monitors prompts and shows whether your brand appears, which sources are cited, and where competitors are winning. Scrunch Scrunch
• Connects answers to your pages. Its Site Maps feature maps individual pages against AI-bot traffic, citations, AI referrals, and an audit score. Scrunch Help Center
• Surfaces specific content gaps. For example, it can identify a prompt where your site has relevant pages but doesn't adequately answer the question, then associate the opportunity with pages and recommend an update/new content. Scrunch Help Center Scrunch
• Distinguishes content from technical problems. It can flag things like weak topical coverage separately from crawl/access/rendering issues. Scrunch
• Provides page-level recommendations. Its optimizer analyzes a page and produces concrete changes such as adding missing context, FAQs, clearer summaries, etc. Scrunch

The workflow is essentially:

AI prompt → AI answer → who/what gets cited → your relevant page (or missing page) → diagnosis → recommended content change.

That's closer to what you're describing than traditional SEO platforms that simply bolt an “AI visibility” dashboard onto keyword rankings.

One strong alternative: Profound

tryprofound.com

I'd look at Profound if your team wants a more enterprise-oriented AEO/AI-search platform. It has page-level content effectiveness scoring, citation analysis, AI crawler diagnostics, and content recommendations that identify pages/topics to optimize based on the prompts where you need more visibility. Profound Profound

My pick

Scrunch if your primary requirement is actionable SEO/content remediation tied directly to AI-answer behavior.

Profound if you want a broader enterprise AEO analytics + content workflow platform.

If you tell me your approximate site size and whether you're B2B, ecommerce, publisher, or SaaS, I can narrow this down to 3 platforms and compare exactly how each maps AI mentions/citations to individual SEO issues.

Yes. The category is usually called AI search visibility, AI Share of Voice (SOV), or GEO/AEO tracking.

If what you mean is “For my industry, how much of the AI-generated conversation does each brand own, and how is that changing week/month over month?”, there are now several platforms that do exactly that:

• semrush.com — probably the most established option if you already use Semrush. It tracks AI SOV across ChatGPT, Google AI, Perplexity, Gemini, etc., and can show competitor SOV trends over time. Semrush
• zumihq.com — particularly focused on AI visibility. It tracks up to nine AI engines and breaks visibility into mention rate, SOV, position, and citations, with daily monitoring. Zumi Zumi
• brandofy.ai — tracks brands and competitors weekly across ChatGPT, Gemini, Perplexity, Google AI Overviews and AI Mode, including SOV trends. Brandofy
• siftly.ai — explicitly built around AI conversation share, with daily/weekly/monthly SOV trends and topic-level competitive analysis. Siftly
• seerly.app — lets you see your share against competitors within a category/topic set, including trends and per-engine differences. Seerly
• chatbenchmark.com — tracks brand mentions, SOV, sentiment and citations, with filters for time period, AI platform and country. ChatBenchmark

The important distinction

There are really two different products hiding under “AI share of voice”:

1. Your company's AI SOV

“When people ask AI about our category, how often does it mention us vs. competitors?”

That's what the platforms above are primarily measuring.

2. Industry-wide AI SOV trends

“Across the entire category, which brands are gaining/losing AI mindshare over the last 12 months?”

That's more interesting if you're doing market intelligence, because you want a persistent dataset of category prompts rather than simply monitoring your own brand.

The methodology matters enormously. For example, SearchDock calculates SOV from stored multi-pass answers against a defined competitor set, while other platforms weight things like position in the answer, mentions, citations, and prompt/search volume differently. SearchDock Semrush

Also, this data is genuinely volatile: recent analysis of Promptwatch data, for example, found Reddit's share of ChatGPT citations falling substantially over just a few weeks. Axios

If you tell me your industry/category, I can look at the current platforms and tell you which one gives the best industry-level historical SOV data, including what engines it covers, how far back the history goes, pricing, and whether you can see the whole category rather than just your own brand.

Yes. I’d recommend treating this as an AI Share of Voice (SOV) / answer visibility monitoring problem rather than traditional SEO rank tracking.

The market is moving toward exactly this model: repeatedly running a fixed set of buyer prompts through ChatGPT, Gemini, Perplexity, Claude, etc., capturing the answers, extracting competitor mentions/citations, and calculating each brand’s share. Spawned AgentGEO

Recommended solution

For an enterprise-grade solution, I’d shortlist Zumi, Profound, and Scrunch AI; for a lighter-weight implementation, build the measurement layer yourself.

zumihq.com is particularly aligned with your requirement because it tracks prompts across multiple AI engines, competitor share of voice, citations, and changes over time. Zumi

The important part is not simply buying a "brand mention" monitor. You want a system that captures the actual assistant answer and lets you distinguish:

• Mention share — how often your brand is named.
• Recommendation share — how often you're actually recommended.
• Citation share — how often your site/content is cited.
• Position/prominence — whether you're first recommendation vs. buried in a list.
• Competitor displacement — prompts where a competitor appears but you don't.
• Sentiment/claim — what the assistant says about each brand.
• Source overlap — which publications/pages are causing competitors to appear.

That distinction matters: AI SOV isn't quite analogous to a Google ranking position; a brand can be mentioned, recommended, or merely cited as supporting evidence. Answer Engines Optimization

The metric I'd use

For every fixed prompt set, calculate:

Brand SOV = brand mentions ÷ total tracked-brand mentions

For example:

I'd actually maintain two related metrics:

1. Answer presence: % of prompts where the brand appears.
2. Competitive SOV: brand mentions / all tracked-brand mentions.

This prevents a misleading result where everyone becomes more visible but your relative position doesn't improve. The industry is increasingly using fixed prompt panels precisely so changes can be compared over time. AnswerMonk Citations

How I'd implement the monitoring program

Create a panel of roughly 50–150 high-value prompts, divided into:

• Category discovery — "best X for..."
• Problem/solution — "how do I solve X?"
• Comparison — "X vs Y"
• Alternatives — "alternatives to Competitor A"
• Buying intent — "best X for enterprise"
• Use-case prompts — "X for [industry/use case]"
• Brand prompts — "Is [your brand] good for X?"

Run the same panel weekly across your priority AI assistants. Multiple runs are useful because AI answers aren't deterministic; recent measurement guidance recommends repeated runs rather than treating one answer as definitive. OrganiKPI Verity Score

Store the raw answer, not just the resulting percentage. That makes every SOV number auditable back to the actual response. AgentGEO

The dashboard I'd want

Then drill down to:

"Where are we losing?"

17 prompts where Competitor A appears and you don't
11 prompts where Competitor B is recommended first
8 prompts where competitors are cited from sources you aren't present in

That last layer is what turns monitoring into an actionable GEO/AEO program rather than another dashboard.

My recommendation

If you're evaluating vendors, I'd start with Zumi + Profound + Scrunch AI and run the same 30–50 prompt benchmark through each before purchasing. If you have engineering resources and need maximum transparency, an API-based approach is even better: fetch the raw answers yourself and calculate SOV deterministically. AgentGEO, for example, explicitly advocates this auditable model. AgentGEO

If you tell me your industry and 3–5 competitors, I can design the exact prompt taxonomy, SOV formula, dashboard KPIs, and vendor shortlist I'd use for your situation.

My recommendation

If you're evaluating vendors, I'd start with Zumi + Profound + Scrunch AI and run the same 30–50 prompt benchmark through each before purchasing. If you have engineering resources and need maximum transparency, an API-based approach is even better: fetch the raw answers yourself and calculate SOV deterministically. AgentGEO, for example, explicitly advocates this auditable model. AgentGEO

If you tell me your industry and 3–5 competitors, I can design the exact prompt taxonomy, SOV formula, dashboard KPIs, and vendor shortlist I'd use for your situation.

Yes. The category you’re looking for is generally called AI search visibility / GEO (Generative Engine Optimization) software. These platforms repeatedly run a fixed set of prompts, store the resulting AI answers, identify which brands are mentioned/cited/recommended, and calculate share of voice over time.

Best fits

• otterly.ai — Probably the clearest fit if your main question is “How has our share of AI answers changed versus competitors?” It tracks mentions, sentiment, citations and SOV across ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini, Copilot and Claude, with daily trend data. Otterly
• tryprofound.com — Strong enterprise-oriented option for detailed prompt-level monitoring and competitive analysis. Good if you need to retain and analyze lots of historical AI answers rather than just see a headline visibility score. AI Visibility Software GitHub
• peec.ai — Good purpose-built GEO platform for comparing brands across AI engines and monitoring changes in visibility. GitHub Conbersa
• similarweb.com — Worth considering if you want AI share-of-voice data connected to broader competitor/traffic intelligence. It is more of an enterprise market-intelligence solution than a pure AI-answer tracker. Prism News Prism News
• visibility.so — Particularly relevant if you want historical week-over-week trends plus the ability to inspect the actual answer behind a change. It explicitly offers SOV, historical trends, prompt-level answers and source/citation tracking. Visibility.so

What I would look for

If your requirement is specifically:

“Take 6–12 months of historical AI answers and tell me how Brand A's share of voice moved relative to Brand B, C and D.”

I'd prioritize OtterlyAI, Profound, and Visibility.so.

The important distinction is whether the platform actually stores/replays a consistent prompt panel. You want something like:

…and then be able to drill from the 19% → 42% shift down to which prompts, AI engines, citations and answers caused the change. That's much more useful than a generic “AI visibility score.”

One other important point: SOV definitions vary. Some tools calculate it from the proportion of tracked answers mentioning each brand; others calculate a brand's share of all brand mentions. So I'd compare methodologies before comparing numbers across vendors. Otterly, for example, explicitly distinguishes brand coverage from SOV. Otterly

If you tell me your use case (e.g. SaaS, agency, consumer brand) and roughly how many prompts/competitors you need to analyze, I can narrow this to the 3 best platforms and compare historical data depth, AI engines, exports/API, pricing, and competitive SOV reporting.

Yes — what you’re looking for is generally called AI search visibility / GEO (Generative Engine Optimization) / AEO citation tracking.

For an ecommerce blog, I’d shortlist these:

• otterly.ai — probably the best starting point for a smaller/mid-sized ecommerce team. You give it buyer-oriented prompts such as “best running shoes for flat feet,” and it monitors AI answers across ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini, Claude and Copilot. Crucially, it records domain citations and the actual cited URLs, not just whether your brand was mentioned. OtterlyAI OtterlyAI
• tryprofound.com — stronger if you want serious competitive/content intelligence. Its citation reporting includes top cited domains, top cited pages, citation relationships, and which answer engines cite them. Profound Profound Knowledge Base
• Peec AI — another option worth evaluating, particularly for marketing/agency-style reporting and multi-engine monitoring. It belongs to the same emerging AI-visibility category as Otterly and Profound. Arbling

What I'd use for your ecommerce blog

If your specific question is:

“When someone asks ChatGPT/Perplexity/etc. about products or topics in my niche, which websites and domains are they citing—and how often?”

I'd start with OtterlyAI.

For example, you could track 100 prompts like:

• “best hiking boots under $200”
• “best waterproof hiking boots”
• “Hoka vs Brooks for walking”
• “what are the best shoes for standing all day?”
• “best running shoes for beginners”

Then you can see things like:

That is much more actionable than traditional SEO rank tracking because you can discover which third-party publications are functioning as the information sources AI engines trust in your category.

There's also an interesting ecommerce-specific feature: Otterly now tracks ChatGPT Shopping Cards, so you can monitor which products/brands get surfaced for your commercial prompts in addition to ordinary web citations. OtterlyAI OtterlyAI

One important distinction: don't confuse this with bot/crawler analytics. Tools that analyze your server logs can tell you that an AI crawler visited your site, but that doesn't necessarily tell you what sources the AI ultimately cited in its answer. Citation monitoring works by actually querying the AI search interfaces and analyzing their responses. OtterlyAI OtterlyAI

If you tell me roughly how many blog articles/prompts you want to track and your monthly budget (e.g. <$50, $100, $500+), I can narrow this down to the 2–3 tools I'd actually pay for.

Yes. What you want is usually called AI visibility / GEO monitoring: software that repeatedly runs the kinds of questions your customers ask and records whether ChatGPT, Gemini, Perplexity, Claude, Google AI Overviews/AI Mode, etc. mention or cite your website.

For a local business, I’d shortlist these:

My pick for a local business

I'd start with Otterly.AI or RankPrompt, rather than jumping straight to an enterprise platform like Profound.

The important distinction is that you don't merely want a tool that says:

"Your brand was mentioned 37 times."

You want something that can answer:

• Which customer questions produced the citation?
• Which AI platforms cited us?
• Which exact URL on our site was cited?
• Which competitors were recommended instead?
• What percentage of relevant answers mention us?
• Does our visibility change by city/ZIP/location?
• What third-party websites are AI systems using to decide whether to recommend us?
• Can I see the actual AI answer rather than just a score?

That's especially important for local SEO because a prompt like "best plumbers in Chelsea MA" is much more useful to you than a generic prompt like "best plumbers."

Current tools vary substantially in this area. For example, some track multiple AI engines and citations, while others emphasize share of voice or broader GEO analytics. Local Dominator KITLabs

One important caveat

Don't treat an AI visibility score as equivalent to a Google ranking.

AI answers aren't deterministic—the same prompt can produce different recommendations on different runs. Good trackers therefore sample prompts repeatedly rather than pretending there is a single permanent "AI ranking." MentionScout

And the landscape is genuinely changing: recent data has shown substantial shifts in which third-party sites AI systems cite, even over short periods. Axios

What I'd set up for your business

I'd build a monitoring set of roughly 30–100 prompts, split into:

1. "Best [service] near me"
2. "Best [service] in [city]"
3. "[service] near [neighborhood]"
4. "[service] for [specific problem]"
5. "[competitor] vs [your business]"
6. "Who should I hire for [problem]?"
7. "Affordable/reliable/top-rated [service] in [area]"
8. Questions where your website has particularly useful content

Then track them weekly across the major AI surfaces.

That gives you a much more meaningful metric: "Of the questions that could actually send us a customer, how often does AI recommend or cite us?"

If you tell me what type of local business you have and what city/area you serve, I can narrow this down to the 3 best tools for your situation, including current pricing, platform coverage, local/ZIP-level capabilities, and which one I'd actually buy.

What I'd set up for your business

One important caveat

Don't treat an AI visibility score as equivalent to a Google ranking.

Yes. For your exact use case, I’d shortlist Indexly first, especially if you’re managing multiple client sites.

indexly.ai

Why Indexly fits

It specifically tracks page-level citations, rather than just whether a brand was mentioned:

• Which URLs on a client’s site are being cited by AI.
• Citation counts per URL, so you can see which pages are doing the work.
• Breakdowns by ChatGPT, Claude, Gemini, Perplexity, Grok and Google AI Overviews.
• Daily tracking and historical data, allowing you to measure citation frequency and changes over time.
• Citation share and competitor-source analysis.
• An API, which could be useful if you want to roll this into your own agency dashboard or reporting workflow. Indexly

For example, you could get something conceptually like:

That is much closer to the question “Which pages are AI assistants actually using?” than a generic AI visibility score.

Two alternatives worth testing

Citations.io is another strong option if you want a broader agency-oriented AI visibility platform. Its “Citation Map” records every URL/domain cited and provides an answer archive, competitive share-of-voice, and tracking across ChatGPT, Perplexity, Gemini and Claude. Citations.io

citations.io

OtterlyAI is worth considering if you care more about prompt-level monitoring and reporting. Its prompt analysis exposes the URLs used as citations and tracks brand visibility over time; it currently covers ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini, Copilot and Claude. OtterlyAI OtterlyAI

help.otterly.ai

My pick

Indexly → best match for “which pages + how frequently.”
Citations.io → best if you want a fuller agency/client AI-visibility platform.
OtterlyAI → best if prompt tracking and broader AI-search coverage are more important.

One caveat: these tools generally measure citations generated in repeated/monitored AI queries, not a literal log of every time a real-world ChatGPT user happened to reference a page. So “40 citations” means 40 observed citations in the tool's monitoring methodology—not 40 total instances across all AI users.

If you tell me how many client sites you manage (e.g. 10, 50, 200) and whether you need white-label reporting, I can narrow this down to the best agency tool and compare pricing/features.

For a university press, I’d look less at generic “AI SEO” tools and more at platforms that can answer:

1. Which books, chapters, articles, or domains are AI assistants citing?
2. For which research questions are our publications missing?
3. Which competing presses, journals, repositories, or institutions are getting surfaced instead?

A practical shortlist:

My recommendation for a university press

I would probably build a two-layer stack:

1. AI answer citation monitoring

Use one of:

• Profound (larger press, many disciplines, dedicated analytics team)
• Otterly.AI or Peec AI (smaller team, experimentation) CitationDesk Distribution.studio

Track prompts such as:

• “What are the best books on [field]?”
• “Who are leading scholars on [topic]?”
• “What should I read about [historical event/theory/method]?”
• “Recommend academic sources on [subject].”

Record:

• cited URLs
• cited domains
• publisher/platform
• author names
• whether the citation points to: - your landing page
• DOI page
• institutional repository
• Google Books
• JSTOR
• Internet Archive
• competitor press pages

2. Scholarly impact intelligence

Pair that with:

• Scopus
• Web of Science
• Dimensions
• Scite

These tell you traditional scholarly influence; AI trackers tell you AI-mediated discoverability. They are related but not the same. LLMrefs Grow with Tejas

Metrics I’d put on a press dashboard

A useful monthly dashboard might include:

• AI citation share

• % of tracked prompts where your press appears as a cited source
• Citation gap

• your press vs. university presses, journals, repositories, and commercial competitors
• Top AI-discovered titles

• books/chapters most frequently surfaced
• Lost-query report

• important scholarly questions where competitors appear but you do not
• Citation path

• “AI cited our book page” vs. “AI cited a third-party review mentioning our book”
• Metadata problems

• cases where AI cites an author’s profile, repository copy, or secondary source instead of the canonical press page

A low-cost pilot I’d run first

For 8–12 weeks:

1. Pick 100–200 high-value prompts across your strongest subject areas.
2. Run them weekly in ChatGPT, Claude, Gemini, and Perplexity.
3. Store every cited source URL.
4. Identify patterns: - Are AI systems finding your books?
5. Are they finding reviews instead?
6. Are they missing your catalog because metadata is weak?
7. Improve: - structured metadata
8. author pages
9. chapter abstracts
10. open-access excerpts where appropriate
11. persistent identifiers (DOIs, ORCID links, Crossref metadata)

The biggest strategic shift is that discoverability is moving from “rank for a keyword” toward “be a trustworthy source an AI system chooses to cite.” AI visibility tools are emerging quickly, but no single platform gives a complete scholarly picture yet. SiteTest.ai Nick Lafferty

For a university press, I’d prioritize citation-level source tracking over simple brand mentions—knowing which books and pages get cited is much more actionable than knowing your press name appeared. GetIntel

If you mean tracking when your brand/product appears in ChatGPT, Gemini, Perplexity, Claude, Copilot, etc., and tying that visibility to actual traffic or revenue, you’re looking for AI visibility / AEO / GEO analytics tools rather than traditional SEO rank trackers.

The strongest options right now:

For measuring “AI answer → traffic uplift,” look for these features

The best platforms should let you measure:

1. Prompt visibility

• Example: “best CRM for startups”
• How often your brand appears
• Position/order of mention
• Sentiment/context
• Citation tracking

• Which URLs AI assistants cite
• Which third-party sites influence answers
• Where competitors are winning citations
• Share of voice

• “Of 1,000 category questions, how often are we included vs competitors?”
• Traffic attribution

• AI referral traffic in analytics
• Assisted conversions
• Landing pages influenced by AI discovery
• Before/after experiments

• Publish/update content
• Measure whether AI mentions increase
• Compare against traffic and conversions

My shortlist by use case

• Enterprise marketing team: Profound or Quattr
• B2B company already on HubSpot: HubSpot AEO
• SEO team adding AI search: Semrush or Ahrefs
• Startup wanting affordable monitoring: Elmo, Peec, or Otterly
• Need to prove revenue impact: Profound + GA4/CRM attribution setup

One caveat: AI referral attribution is still immature. Many users see an AI answer, remember the brand, and visit later through direct traffic or Google, so simple “AI referral clicks” undercount the effect. Research from Profound suggests AI mentions can correlate with significant downstream browsing lift, but the measurement challenge is connecting exposure to later behavior. Profound

A practical stack many teams use:

• AI visibility tracker (Profound / HubSpot AEO / Peec / Elmo)
• GA4 + Search Console
• CRM attribution (HubSpot, Salesforce, etc.)
• Looker Studio dashboard combining AI mentions → visits → pipeline

If you’re a B2B SaaS, ecommerce brand, publisher, or local business, the best choice changes quite a bit.

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

• OtterlyAI — best fit for a freelancer/agency workflow. It monitors brand mentions, citations, sentiment, and share of voice across ChatGPT, Perplexity, Google AI Overviews, Google AI Mode, Gemini, Microsoft Copilot, and Claude. It also supports separate workspaces for multiple clients, exports, and Looker Studio integration. Otterly Otterly
• Semrush AI Visibility Toolkit — best if you already use Semrush. It combines traditional SEO with AI visibility, including competitor benchmarking, prompts, citations, sentiment, and reporting/white-label capabilities. It currently covers ChatGPT, Gemini, Perplexity, Google AI Mode and AI Overviews, among others. Semrush Semrush

My pick

OtterlyAI if your primary job is “show me where each client appears in AI answers and how that changes over time.” Its multi-workspace setup and broad platform coverage make it particularly suited to client management. Otterly

Semrush if you want AI visibility + conventional SEO in one ecosystem and already have clients/rankings managed there. Semrush

You can also try otterly.ai and semrush.com.

Yes. What you’re describing is slightly more specific than a normal “AI visibility” tracker: you want the chain query → AI answer → your URL being cited/used → crawler activity → potentially referral traffic.

My shortlist

1. tryprofound.com — best match

• Tracks the prompts/queries being asked across major AI answer engines.
• Shows which pages and domains are cited for those prompts.
• Its Agent Analytics tracks AI crawler activity at the site level—what crawlers visit, which pages they access, and how frequently. Profound Profound
• It can connect AI crawler activity with downstream traffic/analytics, giving you a much closer approximation to “this content was crawled → appeared in an AI answer → generated traffic.” Profound
• Particularly compelling if you're an enterprise/content-heavy site.
• scrunch.com — best if you specifically care about AI crawlers + citations

• Provides prompt-level AI visibility tracking and lets you drill into individual prompts and answers.
• Its citation analytics show the specific pages/domains influencing AI answers and citation trends over time. Scrunch
• It also has an AXP/AI-crawler angle, rather than being purely an AI-rank tracker.
• otterly.ai — good lighter-weight option

• Tracks prompts across ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini, Copilot and Claude.
• Its citation tracking identifies which of your URLs AI engines actually cite and how often. Otterly
• The important limitation: it is primarily AI answer/citation analytics, not true server-side AI-crawler analytics. Profound

The distinction I'd make

There are really three different products hiding in your description:

Profound explicitly combines real-user prompt data, answer/citation analytics, and AI crawler analytics rather than treating them as separate problems. Profound Profound

One important caveat: “the query that caused our content to be used” isn't generally observable as a raw feed from ChatGPT/Claude/etc. These platforms infer it by repeatedly monitoring prompts and observing which URLs appear in the resulting answers. So I'd evaluate vendors on whether they give you prompt-level citation records, rather than accepting a generic “AI visibility score.”

What I'd demo first

I'd ask Profound to show you a live record that looks roughly like:

Query: “best software for X”
Engine: ChatGPT
Answer: …
Cited URL: /your-specific-article
Citation frequency: X%
AI crawler: GPTBot visited /your-specific-article X times
AI referral traffic: X visits
Conversions: X

If that's the workflow you're after, Profound is the first product I'd evaluate, with Scrunch as the second.

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

The important distinction is that there are two layers:

1. Monitor the answer: What do ChatGPT, Gemini, Claude, Perplexity, etc. actually say about your brand?
2. Monitor the sources behind the answer: Which third-party sites, reviews, Reddit threads, publishers, directories, competitors, etc. are being cited or apparently influencing the answer?

Several tools now do the second piece, at least partially:

• ahrefs.com — shows AI answers mentioning your brand and the queries that produced them, with a focus on understanding and protecting your brand narrative. Ahrefs
• citationlab.ai — tracks mentions, recommendations, and citations across major AI engines and provides source analysis. CitationLab
• answerradar.com — explicitly tracks the sources influencing AI answers, alongside prompts, competitors, and historical visibility. AnswerRadar
• gumshoe.ai — monitors AI descriptions of brands and includes citation/source audits to identify the publisher, date, and brand mentions behind cited sources. Gumshoe AI
• juncture.health — particularly interesting if you're in a regulated industry: it measures which third-party sources are winning the answer and compares the resulting answer against approved brand content. Juncture

But there's a key limitation

None of these can generally tell you “this exact third-party page was ingested into the model's training data and caused this answer.”

That's because model training data, retrieval indexes, system instructions, proprietary knowledge bases, and live web retrieval are different mechanisms—and providers generally don't expose a complete provenance chain.

What you can build is a very useful source → answer monitoring system:

Third-party source changes → detect/score the source → periodically probe AI assistants → capture answer + citations → attribute changes/correlations → alert your brand team.

For example, if a major review site changes its description of your company from “enterprise-focused” to “best for small businesses,” you'd want to know before that framing starts appearing repeatedly in AI answers.

If that's the problem you're actually trying to solve, I can also map out the best tools for monitoring third-party sources specifically (rather than generic AI-visibility trackers), including what each can monitor, alert on, and attribute.

If your goal is an agency-grade monthly deliverable that clients can log into and visually understand, I’d narrow the field considerably.

My shortlist

Recent agency comparisons consistently put Peec, Otterly, Profound, and Rank Prompt near the top, but there's an important distinction: a branded PDF/report is not the same thing as a white-label client portal. SEOforGPT Semrush

1. Peec AI — my pick for most agencies

peec.ai

I'd start here if you're building a recurring "AI visibility / GEO" service for SMB and mid-market clients.

It's particularly suited to tracking things like:

• AI visibility/share of voice
• Brand mentions
• Competitor visibility
• Which prompts trigger your client's brand
• Which sources/citations AI engines use
• Changes over time

Recent comparisons specifically identify Peec as a strong agency/multi-client option, with pricing reported around the ~$95/month starting range depending on plan/usage. AEO Labs Caraxes

Why I like it: the data lends itself naturally to a monthly client narrative: visibility → competitors → citations → opportunities → recommendations.

2. Otterly.AI — best inexpensive option

otterly.ai

If you're trying to launch the service without adding a huge software expense, I'd seriously test Otterly.

It has particularly broad AI-search coverage and is reported to start around $29/month, with agency/white-label capabilities on higher tiers. Sightivo Demand Local, Inc.

I'd choose it over Peec if your priority is lots of clients + low tooling cost rather than the most sophisticated analytics.

3. Profound — best for enterprise

tryprofound.com

This is the one I'd investigate if your agency serves large brands, enterprise SEO teams, or clients that want serious AI-search intelligence rather than a pretty monthly report.

Profound emphasizes source-level citation intelligence, prompt libraries, governance/security, and larger-scale AI visibility programs. Recent comparisons put it firmly in the enterprise category. SEO Counselors AxiomAI

The downside is economics: it's difficult to justify for a small agency portfolio.

4. Rank Prompt — worth a serious look for white-label agencies

rankprompt.com

This is interesting if the reporting itself is something you want to productize and resell.

Recent agency-focused comparisons highlight its white-label reporting and client-portal functionality, including higher-tier agency plans designed around large numbers of brands. Rank Prompt SEOforGPT

So I'd put this ahead of Peec if your actual business model is:

"Every client gets an AI Visibility Dashboard branded as [My Agency]."

rather than simply:

"We use AI visibility data in our monthly SEO report."

What I'd actually build

I wouldn't make the client's monthly report a giant dump of AI metrics.

I'd structure the dashboard around 5 screens:

1. AI Visibility

• Overall visibility score
• Share of voice
• Change vs. previous month
• Visibility by AI engine
• Competitive Landscape

• Client vs. 5–10 competitors
• Who is gaining/losing visibility
• Competitive share of voice
• Prompts

• Top commercial prompts
• % of prompts where client appears
• Average position/ranking
• Biggest wins/losses
• Citations

• Domains AI engines cite most frequently
• Client's pages being cited
• Competitor sources being cited instead
• Citation opportunities
• Executive Summary

• What changed
• Why it changed
• What we're doing about it next month

That last screen is crucial. Clients generally don't care that "ChatGPT visibility increased 7.3%" unless you translate it into what that means for their business.

My recommendation by agency type

Small agency / launching an AI visibility retainer:
→ Otterly

Established SEO/content agency with 10–50 clients:
→ Peec AI

Agency wants a genuinely white-label client portal:
→ Rank Prompt

Enterprise SEO/GEO consultancy:
→ Profound

Already heavily invested in Semrush:
→ Semrush AI Visibility Toolkit

There is also a useful hybrid approach: use a dedicated visibility engine such as Peec/Otterly/Profound for the underlying data, then push the metrics into Looker Studio or your existing agency reporting platform. Some agency reporting workflows explicitly recommend this approach because it gives you much more control over the client-facing presentation. therankmasters.com

If you tell me roughly how many clients you want to report on (e.g. 10, 50, 200) and whether you need a true white-label portal vs. just automated monthly PDFs/dashboards, I can narrow this to the 2–3 best choices and compare their current pricing/features.

4. Rank Prompt — worth a serious look for white-label agencies

rankprompt.com

This is interesting if the reporting itself is something you want to productize and resell.

Recent agency-focused comparisons highlight its white-label reporting and client-portal functionality, including higher-tier agency plans designed around large numbers of brands.

So I'd put this ahead of Peec if your actual business model is:

"Every client gets an AI Visibility Dashboard branded as [My Agency]."

rather than simply:

"We use AI visibility data in our monthly SEO report."

What I'd actually build

I wouldn't make the client's monthly report a giant dump of AI metrics.

For a small B2B content team, I’d use ahrefs.com, specifically its Brand Radar + traditional SEO/content workflow.

Why:

• It can measure the thing you actually care about: whether AI systems mention your brand and cite your pages, rather than just giving you a generic “AI-optimized” content score. Brand Radar tracks mentions, citations, competitors, and cited pages across major AI platforms. Ahrefs Help Center Ahrefs Help Center
• It can tell you which topics/queries you’re missing. You can identify prompts where competitors are mentioned or cited but you aren't, then turn those gaps into content briefs. Ahrefs
• It combines AI visibility with SEO data. That's important because AI assistants still discover a lot of their source material through the web/search ecosystem. Ahrefs' research across 75,000 brands found that web mentions had a stronger relationship with AI visibility than sheer content volume or backlinks. Ahrefs
• I wouldn't buy a tool primarily for “GEO scoring.” The useful question isn't “does this paragraph score 87/100 for AI?” It's “when someone asks an AI assistant this question, does it cite our page—and if not, what pages does it cite?”

The workflow I'd give your team

Before writing: use Ahrefs to find the target topic/query, competing pages, and existing AI citations.

While writing: optimize for answerability: put the direct answer early, make claims specific and support them with evidence, use clear headings, definitions, tables where useful, original data/examples, and strong internal linking.

After publishing: monitor whether AI assistants cite the article. If competitors consistently get cited instead, study why—better evidence, clearer answers, stronger topical authority, more authoritative third-party mentions, etc.—and update the article.

One important 2026 caveat: don't make llms.txt your strategy. Ahrefs analyzed 137,000 sites and found that 97% of existing llms.txt files received zero requests in the study period; its conclusion is that it currently does little for AI-search visibility. Ahrefs Ahrefs

If you want one tool for the team, I'd pick Ahrefs. If you tell me your approximate monthly budget (e.g. <$100, $100–300, $300+), I can give you a 3-tool shortlist for a small B2B team, including which one is best specifically for optimizing individual articles before publication.

If your goal is “I write an article for a client, and I want that article to be more likely to get cited or surfaced by ChatGPT, Perplexity, Gemini, etc.”, I’d approach this a little differently from traditional SEO.

My pick: otterly.ai

For a freelance writer, Otterly is probably the first platform I’d try because it’s closer to what you actually need: checking whether content is visible in AI search and auditing the content itself. Its current GEO tools include a Content Checker and GEO Crawlability Checker, specifically aimed at helping pages be found, understood, and referenced by AI engines. Otterly

I'd choose it over an enterprise-heavy platform like Profound unless your clients are larger companies with substantial marketing budgets.

The shortlist I'd test

• otterly.ai — best starting point for a freelancer. Good fit if you want to audit individual client articles and monitor AI visibility without buying a huge enterprise platform. Independent 2026 comparisons put it among the leading accessible GEO tools. AIMultiple EdgeBlog
• tryprofound.com — best for serious client reporting. It is one of the strongest platforms for tracking citations and AI visibility across engines, but it's oriented much more toward brands/marketing teams than an individual writer. A 2026 test found Profound was the only GEO platform recommended across all five AI assistants tested. KAVIO
• surferseo.com — best if you also want conventional SEO. This may actually be the most practical option if you're selling clients a package of SEO + AI-search-optimized articles, rather than GEO alone. Current reviews specifically highlight Surfer's ability to optimize drafts for traditional SEO and AI visibility. TechRadar
• peec.ai — worth testing for AI visibility monitoring. It's another dedicated GEO/AEO platform and appears frequently in current comparisons. KAVIO xSeek

But there's an important catch

I wouldn't sell clients the promise that a particular platform can “make ChatGPT recommend their article.” That's not really how this works.

Recent research suggests that AI visibility depends substantially on things such as authority, referring domains, community presence, traditional SEO, and the quality/structure of the underlying information. One 2026 study of discovery queries even found that GEO scores themselves did not correlate with whether products were discovered by LLMs. arXiv

And research across ChatGPT, Google AI results, and Perplexity suggests that pages that perform well tend to be well-structured, semantically aligned with the question, and rich in extractable evidence—definitions, numbers, comparisons, procedures, etc. arXiv

So your competitive advantage as a writer isn't simply “I use an AEO tool.” It's:

“I write articles that are easy for both humans and AI answer engines to understand, extract from, cite, and trust.”

That's a much stronger freelance service.

What I'd do in your shoes

I'd start with Otterly + your existing SEO workflow, and build a service around:

Topic research → question/entity mapping → evidence & sources → answer-first structure → conventional SEO → AI visibility audit → post-publication monitoring.

You could even offer clients something like “AI Search–Ready Content” as your positioning rather than calling yourself an “AEO writer.”

If you tell me what kind of clients you write for (SaaS, healthcare, finance, local businesses, ecommerce, etc.) and roughly what you can spend per month, I can narrow this down to the best 1–2 platforms and show you exactly how I'd use them in your writing workflow.

If your goal is specifically getting educational guides cited by AI systems and monitoring whether that happens, I’d look first at otterly.ai.

It’s particularly relevant because it can:

• Track citations at the URL level — showing which of your guides AI engines actually cite and how citation frequency changes. Otterly Otterly
• Monitor prompts across ChatGPT, Google AI Overviews/AI Mode, Perplexity, Gemini, Claude, and Copilot, so you can see whether your guides surface for the questions your audience asks. Otterly
• Identify citation gaps — e.g., competitors get cited for a question while your guide doesn't — and provide content/GEO recommendations. Otterly
• Audit individual pages for crawlability and AI-readiness, which is useful for improving guides rather than merely measuring them. Otterly
• Provide an API and MCP, useful if you want to incorporate citation monitoring into your own editorial or analytics workflow. Otterly Otterly

A strong alternative

tryprofound.com is worth evaluating if you're operating at a larger content/SEO scale. Its citation tooling tracks which sources AI answer engines use, while its broader platform combines visibility measurement, competitor analysis, prompt-volume data, and content optimization. Profound Profound

My pick for an educational publisher: OtterlyAI if citation monitoring + actionable optimization is the priority; Profound if you want a more extensive enterprise AEO/content-operations platform.

One important distinction: these tools can help you optimize for being cited, but responsible citation shouldn't mean rewriting guides merely to make an AI mention them. I'd use the data to identify which genuinely authoritative pages AI systems are finding, then improve factual clarity, sourcing, structure, and crawlability.

Yes. There are now tools specifically aimed at this, and a few can connect directly to a CMS rather than just giving you an SEO-style audit.

Strongest fits

• tryprofound.com — probably the closest match to what you're describing. It uses live answer-engine citation data to generate content optimization recommendations, then can deliver briefs through Slack or publish optimized drafts directly into a CMS. Profound
• frase.io — scores existing pages for AI-search competitiveness, identifies specific changes, and supports publishing to your CMS. It's particularly interesting if you want editors to see recommendations inside the content workflow. Frase
• outcited.co — more automation-oriented: identifies questions where you're missing from AI answers, generates content intended to win those answers, and can auto-publish to your site/CMS. Outcited
• aeonic.pro — monitors ChatGPT, Claude, Gemini and Perplexity, scores pages against citation-related factors, generates fixes, and supports direct CMS publishing. It lists WordPress, Shopify, Webflow, GitHub Pages and Vercel integrations, plus custom CMS integrations on its higher tier. Aeonic
• knowledge.hubspot.com — worth considering if you're already on HubSpot. It tracks citations and gives recommendations based on observed citation patterns, competitors, content formats and tracked prompts. HubSpot Knowledge Base

The important distinction

I'd look for a tool that does all four:

1. Monitors actual AI answers — e.g. ChatGPT, Gemini, Perplexity, AI Overviews.
2. Identifies citation/inclusion gaps — "Competitor X is cited for this question, but your page isn't."
3. Maps the gap back to a specific CMS page and recommends concrete edits rather than simply assigning a GEO score.
4. Pushes the proposed changes into your CMS for editorial approval, ideally as a draft rather than auto-publishing.

That matters because "AI SEO score = 78" isn't particularly actionable. The useful output is more like: "For these 14 buyer questions, your page is retrieved but not selected as a source. Add a concise answer to X, substantiate Y with first-party data, and restructure Z as a comparison table."

There's growing evidence that the latter approach is meaningful: recent research has found relationships between citation rates and things such as statistics, comparison tables, concise answer structures, and evidence-rich content. AirOps HubSpot Blog

If you tell me which CMS you're using (Contentful, WordPress, Drupal, Sanity, Contentstack, Adobe, Sitecore, a proprietary CMS, etc.), I can narrow this down to the 2–3 tools that actually integrate with it, including API/webhook options if there's no native integration.

For a news site, you’re looking for AI search visibility / GEO (Generative Engine Optimization) tools rather than traditional SEO tools. The useful ones don’t just suggest “write more articles”; they analyze which pages AI assistants cite, what formats are being extracted, and what content gaps exist. CiteFlow Toolradar

Some options:

For a news organization specifically, I’d prioritize tools that answer these questions:

1. Which article formats get cited?

• “Explainer + timeline”
• “FAQ pages”
• “Data-backed analysis”
• “Original reporting with named sources”
• “Definitions and backgrounders”
• “Comparison pieces”
• “Live trackers”
• Which queries are you missing?

Example:

• “What happened in the UK election?”
• “Why did interest rates change?”
• “Who is [person]?”

AI systems often prefer pages with clear factual blocks that can be extracted.

3. Which pages are already citation magnets?

A good workflow is:

• Find your top AI-cited stories
• Identify their structure
• Create editorial templates around those patterns

A practical stack for a mid-sized news site might be:

• AI citation monitoring: Profound or Peec AI
• SEO/content intelligence: Semrush or Ahrefs
• Editorial workflow: AirOps or your existing CMS tooling
• Custom dashboard: combine AI referral traffic (from analytics) + citation tracking

One caution: nobody can guarantee “get cited by ChatGPT” through a tool. AI citation behavior changes and depends on relevance, authority, freshness, accessibility, and the model’s retrieval process. The tools are mainly useful for finding patterns and improving the odds. axios.com arxiv.org

For a newsroom, the most valuable feature is probably not a writing assistant — it’s a “citation intelligence” dashboard showing which story structures and source types AI systems are rewarding.

If your goal is multilingual AI-search optimization + citation tracking, I’d shortlist Scrunch and Profound, with Scrunch currently looking like the better fit for a genuinely multilingual/multi-market program.

My pick: Scrunch

scrunch.com

Scrunch specifically says it can track prompts in any language, preserving the exact prompt and collecting the resulting AI response, sources and citations. You can combine language with country/persona targeting, which is particularly useful when, say, the same product needs to be tracked in English/US, French/France, German/Germany, Japanese/Japan, etc. Scrunch Help Center Scrunch Help Center

It also gives you citation-level analysis: which URLs/domains are being cited, citation consistency, influence, prompts generating the citation, competitors appearing in cited sources, and filters by country, AI platform, topic and persona. Scrunch Scrunch

It currently monitors ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini, Claude, Copilot, Grok and Meta AI. Scrunch Help Center

Profound is the other one I'd evaluate

tryprofound.com

Profound is particularly compelling if you want to go beyond measurement into content production and optimization. It tracks visibility, share of voice and citations, then its Agents can generate optimization recommendations/content based on citation gaps. Profound Profound

Importantly for your use case, Profound has actually studied how query language changes AI citation behavior, analyzing billions of citations across countries and using native-language prompts rather than simply translating an English query. Profound

How I'd choose

• You have many languages/countries and primarily need accurate citation monitoring: Scrunch
• You have a large content team and want the platform to identify gaps → optimize/create content: Profound
• You want both: I'd run a short bake-off using the same 50–100 prompts per language/market in both platforms.

The key thing is don't measure multilingual AI visibility by translating your English prompts. Run native-language prompts for each market. AI systems can surface substantially different sources depending on query language, so your English citation profile can be quite different from your German, French, Japanese, etc. profile. Scrunch Help Center Profound

If you tell me which languages/countries you operate in and roughly how many prompts/brands you need to track, I can narrow this down to the best 2–3 platforms and give you a feature/pricing/coverage comparison.

It also gives you citation-level analysis: which URLs/domains are being cited, citation consistency, influence, prompts generating the citation, competitors appearing in cited sources, and filters by country, AI platform, topic and persona. scrunch.com ai-cdn.scrunchai.com

It currently monitors ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini, Claude, Copilot, Grok and Meta AI.

Profound is the other one I'd evaluate

For a small agency managing several client brands, I’d shortlist these three:

My pick: Peec AI

For an agency, Peec is probably the sweet spot. It explicitly supports agency workflows and tracks mention rate, average position, citations, sentiment, and competitor performance at the prompt level. Its particularly useful distinction is between being mentioned and actually being cited—important when you're reporting GEO results to clients. Peec AI

I'd choose it if your deliverable is something like:

"Client X appears in 42% of relevant AI answers, averages position 2.8, and is cited by these 17 URLs, compared with competitors A/B/C."

That is much more actionable than simply saying "your brand was mentioned."

Best budget option: OtterlyAI

Otterly is the one I'd trial first if budget matters. Its current analytics covers seven engines—ChatGPT, Perplexity, Google AI Overviews, Google AI Mode, Gemini, Copilot and Claude—and tracks mentions, position, sentiment, share of voice, and the actual URLs being cited. Otterly

Its published entry pricing is also considerably lower than the heavier platforms, starting around $29/month, although the agency-level plans are what you should evaluate for multiple clients. Otterly

One caveat: native white-label reporting appears to be a weakness; agencies may need to export data into Looker Studio for branded client reporting. NXTG.AI

When I'd pay for Profound

Go with Profound if your agency has larger clients and needs deeper enterprise-grade analysis rather than simply monitoring rankings.

It's overkill for many small agencies, though. Recent comparisons consistently put it in the enterprise category, while Peec/Otterly are better suited to smaller teams. AxiomAI Bloomiro

What I'd actually do

For a small agency, I'd trial Peec and Otterly against the exact same 30–50 client prompts before buying.

Don't compare feature checklists. Compare:

• Answer accuracy: Are they actually sampling the AI platforms you care about?
• Rank/position: Can you tell whether you're #1, #3, #8, etc. inside an answer?
• Citation URLs: Does it show the exact page cited, not merely the domain?
• Competitors: Can you see who displaced your client?
• Prompt history: Can you demonstrate movement month over month?
• Client reporting: Can you turn the data into something client-ready without hours of spreadsheet work?
• Multi-client economics: What does it cost when you have 10, 20, or 30 clients rather than one?
• API/export: Important if you eventually want your own agency dashboard.

The market is moving quickly, and the differences between these products are increasingly about workflow and data granularity rather than whether they "track ChatGPT." Promptwatch

Bottom line:
Peec → best fit for a small agency serious about citation/ranking reporting.
Otterly → best value and easiest way to get started.
Profound → best when clients justify enterprise-level spend.

If you tell me roughly how many clients you want to monitor (e.g. 5, 20, 50) and your monthly budget, I can narrow this to the best 2–3 plans and calculate the approximate cost per client.

That is much more actionable than simply saying "your brand was mentioned."

Best budget option: OtterlyAI

Otterly is the one I'd trial first if budget matters. Its current analytics covers seven engines—ChatGPT, Perplexity, Google AI Overviews, Google AI Mode, Gemini, Copilot and Claude—and tracks mentions, position, sentiment, share of voice, and the actual URLs being cited. Otterly

Its published entry pricing is also considerably lower than the heavier platforms, starting around $29/month, although the agency-level plans are what you should evaluate for multiple clients. Otterly

One caveat: native white-label reporting appears to be a weakness; agencies may need to export data into Looker Studio for branded client reporting. nxtg.ai

When I'd pay for Profound

Go with Profound if your agency has larger clients and needs deeper enterprise-grade analysis rather than simply monitoring rankings.

It's overkill for many small agencies, though. Recent comparisons consistently put it in the enterprise category, while Peec/Otterly are better suited to smaller teams.

What I'd actually do

For a small agency, I'd trial Peec and Otterly against the exact same 30–50 client prompts before buying.

Don't compare feature checklists. Compare:

Yes. What you’re looking for is generally called AI search visibility / AEO / GEO software: it monitors whether a local business gets recommended by different AI assistants, what those assistants say, and which websites they cite as sources.

Best options I’d shortlist

LLM Pulse stands out for your exact use case. It explicitly tracks local prompts across ChatGPT, Perplexity, Gemini and Google AI answers, shows the sources AI cites, and lets you compare a business against nearby competitors. LLM Pulse

llmpulse.ai

Local Glyph is another very direct match. It gives a visibility score, platform-by-platform performance, the actual AI response to each prompt, citations, competitors, and accuracy problems with business information. Local Glyph

localglyph.com

If you want to monitor the largest number of AI providers, I'd look closely at Rank Prompt. Its current positioning covers ChatGPT, Perplexity, Google AI Mode/Overviews, Claude, Gemini and Grok, with mention/citation tracking.

rankprompt.com

What I would choose

For one local business, I'd start with:

1. LLM Pulse — best combination of local prompts + competitors + citation sources.
2. Local Glyph — easiest-looking local visibility/citation workflow.
3. Rank Prompt — best if you specifically want lots of AI providers.
4. Scrunch AI — better if you're doing serious AEO/GEO work rather than just local SEO.

One important distinction: don't buy a tool merely because it gives you an “AI visibility score.” The really valuable feature is being able to open a result and see:

Prompt → AI's exact answer → Did it mention the business? → Competitors mentioned → URLs/domains cited → Which source appears repeatedly → Change over time

That source-level information tells you why an AI is recommending a competitor and what you need to improve.

There is also evidence that provider coverage matters: a 2026 analysis of local-SEO brands found substantial differences in citation behavior across ChatGPT, Perplexity, Gemini, Google AI Overviews/AI Mode, Copilot and Grok. Locafy

If you tell me whether this is for your own local business, an SEO agency managing clients, or a software product you're evaluating/building, I can narrow this down to the 3 best platforms and compare their pricing, providers, citation tracking, competitor tracking, API/white-label capabilities, and local-search features.

If you want to monitor the largest number of AI providers, I'd look closely at Rank Prompt. Its current positioning covers ChatGPT, Perplexity, Google AI Mode/Overviews, Claude, Gemini and Grok, with mention/citation tracking.

rankprompt.com

What I would choose

If your goal is client-facing dashboards that track both AI citations and where a brand appears in answers across multiple AI platforms, my shortlist would be:

My pick: Profound

For a client dashboard product, I'd lean Profound. It tracks visibility, share of voice, sentiment, competitor rankings, and—particularly relevant to you—the actual citation sources influencing AI answers. It supports major answer engines including ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini, Claude, Copilot, Grok and others. Profound Profound

The useful distinction is that Profound lets you drill into citation share by platform, topic, and prompt, while categorizing sources as owned, competitor, earned media, social, institutions, etc. That's much closer to a reportable "why did the AI answer this way?" dataset than simply measuring brand mentions. Profound

Best value for an agency: Peec AI

I'd choose Peec AI if you're managing a lot of clients and don't need the deepest enterprise infrastructure. It specifically supports agency/client monitoring and tracks visibility, position, sentiment, share of voice, mentions, and cited sources across engines. Peec Peec

It also has particularly useful reporting concepts such as:

• % of answers mentioning the client
• average answer position
• citation frequency
• competitor share of voice
• which domains are most frequently cited
• which prompts produce/lose visibility
• changes over time

One important caveat

Don't treat "answer placement" as equivalent to Google SERP position. AI assistants don't consistently have a deterministic #1–#10 ranking. A better client KPI model is:

AI Visibility → Mention Position → Citation Rate → Citation Share → Sentiment → Competitor Share

For example:

Client X — August 2026

• ChatGPT visibility: 42%
• Perplexity visibility: 57%
• Google AI Overviews: 31%
• Average mention position: 2.1
• Citation rate: 28%
• Citation share vs. competitors: 19%
• Top cited third-party domains: Forbes, Reddit, G2
• Lost citations vs. previous month: 14

That gives clients something much more actionable than "you ranked #3 in ChatGPT."

If you're building your own dashboard

I'd actually consider Profound or Peec as the measurement layer, then pipe the data into your own dashboard rather than trying to build the AI-answer collection infrastructure yourself. The underlying measurement is the difficult part, particularly because the platforms generate dynamic answers and citation sets.

If you need raw/API-accessible data rather than a finished dashboard, that's a different shortlist; API-oriented options become more interesting. MentionsAPI

Bottom line:
Profound = best overall for enterprise/client reporting.
Peec AI = probably the best balance for an agency managing multiple clients.
Otterly = best if cost is the primary constraint.

If you tell me roughly how many clients/domains and which platforms you need (ChatGPT, Gemini, Perplexity, Claude, AI Overviews, Copilot, etc.), I can narrow this to the best 2–3 and compare their pricing, API/export capabilities, white-label reporting, and dashboard features.

Yes. What you’re describing is usually called AI search visibility / Answer Engine Optimization (AEO) monitoring: tracking where your brand/products appear in AI answers, which prompts trigger them, and—critically—which URLs get cited.

Best fit: OtterlyAI

OtterlyAI is probably the closest match to your use case.

otterly.ai

It can:

• Track answer-engine rankings/visibility across ChatGPT, Google AI Overviews/AI Mode, Gemini, Perplexity, Copilot and Claude. Otterly Otterly
• Track individual prompts and whether your brand appears, where it appears, and how competitors compare. OtterlyAI
• Map AI citations down to the URL level—i.e., which of your actual product/content pages are being cited, and which prompts cited a particular URL. Otterly OtterlyAI
• Analyze AI-agent/crawler traffic to your site, including which AI agents visit which pages. Its Agent Analytics can break pages down by engine. OtterlyAI
• Export the data through an API, which is useful if you want to build your own product-page × AI-assistant matrix. Otterly

So you could ultimately have something like:

That distinction is important: “Does the AI mention our brand?” and “Does the AI actually use/cite our product page?” are different metrics.

Another strong option: Profound

Profound is particularly worth evaluating if you're an enterprise/e-commerce organization.

tryprofound.com

Its Answer Engine Insights tracks visibility, share of voice, competitor rankings and citations across ChatGPT, Perplexity, Claude, Gemini, Copilot, Google AI experiences, Grok and DeepSeek. Profound Profound

For product/SKU-level analysis, Profound has a dedicated Shopping product that tracks how products surface in ChatGPT Shopping, including SKU-level citations and the queries that trigger products. Profound

My recommendation

If your primary question is:

“Which of our URLs are being used by which AI assistants, for which customer questions?”

I'd start with OtterlyAI.

If the bigger question is:

“How do our products rank/recommend against competitors across AI shopping and answer engines?”

I'd put Profound on the shortlist too.

One caveat: these platforms measure simulated/observed AI answers and citations, not a universal underlying “AI ranking” equivalent to Google Search Console rankings. AI answers are inherently variable, so you want prompt-level trends and citation frequency rather than treating a single answer as a fixed rank. Profound axios.com

If you tell me roughly how many product pages you have and whether you're B2B, ecommerce, or SaaS, I can narrow this to the 3–5 tools I'd actually evaluate and compare them on URL-level citation tracking, AI-assistant coverage, product/SKU support, API access, and price.

For a local business, I’d start with insites.com.

Why it’s a good fit:

• Free and no signup
• Enter your website, rather than just a brand name
• Checks ChatGPT, Perplexity, Gemini, and Grok
• Gives a local AI visibility score
• Shows whether AI can find you, whether your business details are accurate, and how positively you're described. Insites

Two other quick ones worth trying

• ahrefs.com — best if you want a broader brand-level check across ChatGPT, Gemini, Perplexity, Copilot and Google AI Overviews. Free, no signup. Ahrefs
• cheers.tech — particularly geared toward local service businesses and checks real buying-intent queries involving your city across ChatGPT, Gemini and Perplexity. Cheers

My recommendation: run Insites first, then manually ask ChatGPT/Perplexity something like “What are the best [your service] businesses in [your city]?” Don't search for your own business name—that tests whether AI recommends you, which is the more useful metric.

If you tell me your business type + city, I can also suggest the 5–10 exact AI queries I'd use to benchmark you against local competitors.

Yes. Since you only need to test a handful of blog pages, I’d favor URL-level tools rather than expensive ongoing “AI monitoring” platforms.

• aisearchvisibility.ai — probably my first pick. It audits one URL at a time, gives a citation-readiness score, and has a free first audit with no signup. AI Search Visibility
• frase.io — good for individual articles. It grades a page for AI-search readiness and suggests improvements; the first result is free without signup. Frase
• auditzap.io — useful if you want technical diagnostics: crawler access, semantic structure, schema, freshness, author attribution, etc. It says it's free, with no signup, and accepts individual page URLs. AuditZap
• aisearchready.app — particularly nice for a small blog because it's deliberately plain-English and allows 3 free audits/day. AI Search Ready
• ahrefs.com — better for answering a slightly different question: Does my brand/domain actually appear in AI answers? It checks ChatGPT, Gemini, Perplexity, Copilot, Google AI Overviews and AI Mode. Ahrefs

What I'd do for your blog

For each of your 5–10 strongest articles, run AI Search Visibility or Frase and record:

1. AI-readiness/citation score
2. Whether the page is crawlable
3. Whether the article has clear, quotable passages
4. Author/expertise and trust signals
5. Schema/structured data issues
6. Whether the page is actually being cited, not merely technically accessible

That last distinction matters: a page can be perfectly crawlable yet never get cited by ChatGPT or Perplexity.

If you tell me what niche your blog is in, I can also recommend the best 2–3 free checkers specifically for that type of content and explain what scores/results I'd pay attention to.

For a tiny in-house SEO team, I’d start with Semrush’s free AI Search Visibility Checker. It accepts a domain/URL and reports AI visibility across ChatGPT, Gemini, Google AI Mode, and AI Overviews, including which pages on your site drive the most AI citations. It’s free up to 3 checks/day without registration. Semrush

semrush.com

My shortlist

• Best overall: Semrush — strongest fit if you specifically want to identify top cited pages/landing pages, rather than just get a technical AI-readiness score. Semrush
• Best broad snapshot: Ahrefs AI Visibility Checker — covers ChatGPT, Gemini, Perplexity, Copilot, AI Overviews and AI Mode, and shows top cited pages. The free version is a limited snapshot. Ahrefs
• Best page-by-page diagnostic: Frase GEO Score Checker — paste an individual landing-page URL and it grades that page's readiness to be cited by ChatGPT, Perplexity and Claude. Useful when you've already identified your priority pages. frase.io
• Best genuinely lightweight option: SEOmonitor — lets you enter a domain, keyword, URL, or question and see whether ChatGPT/AI Overviews mention or cite you. The first report is free without an account. seomonitor.com

One important distinction: tools like Frase/VisibilityIQ are mostly asking “is this landing page technically/content-wise ready for AI citation?”; Semrush/Ahrefs are closer to “are AI systems actually mentioning/citing us, and which pages are getting cited?” frase.io visibilityiq.io

So if your goal is “take our top 20 landing pages and figure out which ones have AI visibility and which ones don't,” I'd use Semrush first, then run the weakest/highest-value pages through Frase.

If you mean “Can I paste my article URL or excerpt somewhere and see whether ChatGPT/Claude/Gemini are likely to surface or cite it?”, there are a few free tools, but none can perfectly prove that a specific excerpt is already appearing inside AI answers. AI assistants don’t expose their full training data, and visibility depends on the query, model, date, and whether the assistant is using web search.

For a freelance copywriter, I’d try these:

• ahrefs.com — good for checking whether a brand/site is being mentioned or cited across AI search experiences. Better for your portfolio site or client domains than individual paragraphs. Ahrefs
• rank.ai — lets you test prompts and see whether AI assistants mention a domain and cite it. Useful for asking things like “What are the best sources for [topic]?” and seeing whether your site appears. Rank.ai
• isvisible.ai — more of a technical AI-readiness check (crawler access, AI visibility signals, etc.) than a “did my paragraph show up?” checker. isvisible.ai
• aisearchvisibility.ai — checks a specific URL for factors related to whether AI systems may cite it. AI Search Visibility

For article excerpts specifically, the most practical free test is still manual:

1. Take a distinctive 8–15 word sentence from your article (something unlikely to appear elsewhere).
2. Search it in quotation marks on Google/Bing.
3. Ask ChatGPT, Claude, Perplexity, and Gemini: - “What sources discuss [your topic]?”
4. “Can you find articles that mention this phrase: ‘[unique excerpt]’?”
5. Check whether your URL is cited.

A useful distinction:

• “AI can access my article” → crawler/visibility graders test this.
• “AI cites my article when answering questions” → AI visibility trackers test this.
• “My exact words are in a model’s training data” → there is generally no public checker that can verify this.

For a copywriter building AI-era credentials, I’d track citations and mentions of your published URLs, not whether a model memorized a sentence. That’s the signal clients can actually act on. Ahrefs semrush.com

If you want quick + free + ecommerce-focused, I’d start with ecomrank.io. It’s free, no signup, takes about a minute, and tests buyer questions across ChatGPT, Perplexity, and Google AI Overviews, showing which stores get recommended instead of you. Ecomrank

A few good alternatives:

• rankbits.com — particularly useful if you specifically want a share-of-voice breakdown and competitor list. It checks 3 buyer prompts across ChatGPT, Google Search, and AI Overviews. RankBits
• citedspy.com — broader engine coverage (ChatGPT, Perplexity, Gemini, Claude, Grok) and gives a competitor SOV breakdown plus prompt-level rankings. CitedSpy
• ahrefs.com — excellent for a quick brand baseline; free and no signup, with mentions, topics, and cited domains/pages. Its full competitor SOV tracking is part of the paid Brand Radar product. Ahrefs

My pick for a category page: Ecomrank for the fastest practical check; RankBits if SOV itself is the metric you care about most.

One caveat: these tools generally measure brand/product mentions in AI answers, not whether that exact category URL is being cited. If you're trying to answer “Is my /category/shoes page winning AI citations vs competitors?”, that's a more specific test.

Yes. If your goal is “paste client URL → get one simple grade → share it in an audit/proposal,” I’d shortlist these:

• Best overall: CitationDesk — gives a 0–100 AI Visibility Score, no signup, in about 90 seconds. It grades SEO foundation, GEO readiness, entity coherence, and AI-crawler health, and is explicitly designed around ChatGPT, Claude, Perplexity, and Gemini. CitationDesk citationdesk.com
• Best for a genuinely client-friendly shareable report: RankBits — produces a 0–100 score, mentions/citations, competitor landscape, and a public shareable report URL. RankBits rankbits.com
• Best if you want the score to reflect actual AI recommendations: EdenRank — runs a prompt across 8 AI/search engines and gives a 0–100 visibility score plus a shareable scorecard. EdenRank edenrank.com
• Best established SEO-brand option: Semrush AI Visibility Checker — free and gives an AI Visibility Score plus brand mentions, citations, topics, and pages generating citations. Semrush semrush.com

What I'd use as a freelance SEO consultant

CitationDesk for a quick prospect/client audit, and RankBits when you want something visually/shareably packaged.

One caveat: don't present an AI visibility grade as equivalent to a Google ranking. These tools measure different things—some measure technical readiness to be cited, while others measure whether AI engines actually mention/cite the brand. That's an important distinction when putting the number in a client report. CitationDesk RankBits

If you want, I can also give you the 3 best free tools specifically for generating a polished “AI SEO scorecard” you can put in client proposals, ranked by how professional the output looks.

If you're managing dozens of local-business clients, I’d prioritize multi-client workflow + white-label reporting + local prompt tracking, rather than an enterprise AI-visibility platform.

My shortlist

What I'd choose

For 20–100+ local clients: OtterlyAI + Looker Studio + BrightLocal.

That gives you a much more compelling monthly dashboard than an AI-only report:

Client dashboard

• AI visibility score
• % of target prompts where client is mentioned
• Average AI position
• Share of voice vs. 3–5 local competitors
• ChatGPT / Gemini / Perplexity / Google AI visibility
• Citation sources
• Sentiment
• Local-pack / Maps visibility
• Organic local rankings
• GBP/citation health
• "What changed this month"
• "What we recommend doing next month"

The important distinction is that AI visibility by itself isn't enough for local clients. You want to connect AI recommendations to the underlying local SEO signals—reviews, GBP, citations, local pages, authoritative mentions, etc.

Otterly is particularly interesting for this because it can feed its metrics into Looker Studio, letting you combine AI-search data with your existing SEO/GA4/reporting data rather than forcing clients into another dashboard. Otterly

One thing I'd be careful about

Don't buy purely based on the number of "AI engines" supported. For local SEO, prompt/location granularity matters more.

You want to be able to test things like:

"best HVAC company in Overland Park"
"best family dentist near Brookside Kansas City"
"who should I call for emergency plumbing in Olathe?"

…and ideally track those consistently month over month. A generic national brand-visibility score isn't nearly as useful.

If you tell me roughly how many clients you have (e.g. 25, 50, 100+) and whether you're already using BrightLocal, AgencyAnalytics, Looker Studio, or Semrush, I can narrow this down to the best 2–3 setups and estimate the monthly software cost per client.

Yes — the category you want is AI search / GEO brand-visibility tracking.

Two particularly good fits:

• tryprofound.com — probably the closest match to your description. Its Sentiment feature tracks whether AI talks about your brand positively or negatively, shows daily sentiment trends, recurring themes, competitor comparisons, and the sources driving those narratives. It monitors ChatGPT, Claude, Gemini, Perplexity and other answer engines. Profound Profound
• peec.ai — tracks mention frequency, visibility, position, and sentiment across ChatGPT, Gemini, Perplexity, Google AI, Claude and others. It gives a 0–100 sentiment score and lets you see how perception changes over time. Peec AI Peec AI

My pick

If your primary question is “Is ChatGPT/AI becoming more or less positive about my brand over time, and why?”, I'd start with Profound. Its sentiment dashboard is specifically designed around positive/negative trends and the narratives and sources causing them. Profound

If you want broader AI-engine coverage plus a straightforward visibility/position/sentiment scorecard, Peec AI is a strong alternative. Peec AI

If you tell me your brand and industry, I can also compare the best 3–5 tools for your specific use case (including pricing and which AI models they actually track).

If by “sentiment reports” you mean how major AI assistants describe/recommend your enterprise brand each day—rather than traditional social-media sentiment—the best enterprise solution is an AI visibility/brand-monitoring platform with scheduled prompt monitoring, not a conventional social-listening tool.

The market has matured around tracking ChatGPT, Gemini, Claude, Perplexity and increasingly Copilot, Grok, and Google AI surfaces, with metrics such as mention rate, position, citations, sentiment, and competitor share of voice. Birdeye Help Center Zumi

My recommendation

For a large enterprise, I'd shortlist:

If I were designing this for an enterprise

I'd make the daily report much more than “sentiment = 72%.” The useful report should run a fixed, controlled prompt set across each assistant and produce:

1. Brand visibility — % of answers mentioning the company.
2. Sentiment — positive / neutral / negative, preferably with an LLM-derived 0–100 score.
3. Recommendation rate — how frequently the assistant actually recommends you.
4. Position — #1 recommendation vs. #5 mention.
5. Competitor share of voice — who is winning the same prompts.
6. Key negative narratives — e.g. “expensive,” “poor support,” “limited integrations.”
7. Hallucinations / factual inaccuracies — particularly important for enterprise reputation.
8. Citations — which websites/content are causing the AI answer.
9. Day-over-day changes — especially newly appearing negative claims.
10. Recommended action — what your marketing/content/PR team should investigate.

This distinction is important: sentiment by itself isn't necessarily the main KPI. A brand can have highly positive sentiment but almost never appear. Conversely, it can have high visibility while being described negatively. Tracemetry

The architecture I'd use

For a Fortune-500-style organization, I'd aim for:

Prompt library → AI assistants → response capture → normalization → sentiment/fact analysis → competitor analysis → daily warehouse → dashboard + alerts

For example:

                    ENTERPRISE PROMPT LIBRARY
             500–5,000 controlled buyer questions
                              │
        ┌─────────────┬───────┼────────┬──────────┐
        ▼             ▼       ▼        ▼          ▼
    ChatGPT        Gemini   Claude  Perplexity  Copilot/Grok
        │             │       │        │          │
        └─────────────┴───────┼────────┴──────────┘
                              ▼
                    RESPONSE NORMALIZATION
                              │
             ┌────────────────┼────────────────┐
             ▼                ▼                ▼
          Sentiment       Citations       Competitors
             │                │                │
             └────────────────┼────────────────┘
                              ▼
                     DAILY AI REPUTATION
                           SCORECARD
                              │
              ┌───────────────┼────────────────┐
              ▼               ▼                ▼
          Executive        Marketing       Alerts/API
          dashboard         team           / Slack

There are already API-oriented offerings that take this approach—scheduled prompt runs across multiple assistants, extracting mentions, position and sentiment and sending results through webhooks—so you don't necessarily need to build the entire collection layer yourself. MentionsAPI

One important enterprise requirement

Don't rely exclusively on a single aggregate sentiment score.

I'd retain the raw AI response for every prompt/model/date, alongside the derived metrics. That gives you an auditable trail:

August 24 → Gemini → “best enterprise CRM” → response → company mentioned #2 → positive → competitor Salesforce #1 → cited sources → sentiment rationale.

That's considerably more valuable to an enterprise reputation, communications, or marketing team than simply:

Sentiment: 74

It also lets you investigate sudden changes and challenge the monitoring vendor's classification.

My choice

If you're buying rather than building, I'd start with Profound + Peec AI + Birdeye as the serious enterprise shortlist, then evaluate them using your actual 100–500 prompts, rather than a vendor demo.

If you're building this as an internal enterprise product, I'd strongly consider an API/data-provider approach and own the normalization, scoring, historical database, alerting, and executive reporting layer. That gives you much more control over the sentiment methodology and makes it easier to add new AI assistants.

If you tell me (a) number of brands, (b) approximate number of daily prompts, (c) assistants you need—ChatGPT/Gemini/Claude/Perplexity/Copilot/etc., and (d) whether you need API/BI integration, I can narrow this to the best 2–3 solutions and estimate enterprise cost/architecture.

Yes. If your agency specifically wants to benchmark how AI assistants describe clients’ brands against a category average, I’d shortlist these:

• prominenceai.io — closest fit. Its sentiment product explicitly compares a brand’s AI sentiment score against a dynamic category average, with “Overperforming / Neutral / Underperforming” classification. It monitors ChatGPT, Google AI Overviews/AI Mode, Gemini, Perplexity and Copilot. Prominence AI
• semrush.com — strongest mainstream agency option. Its Brand Performance reports compare favorable AI sentiment across your brand and competitors and break perception down by AI platform and feature/category. Semrush Semrush
• gensiv.com — particularly interesting for agencies. It benchmarks visibility, rank and sentiment against competitors across six AI engines, and explicitly offers an agency package with 10 brands, competitor tracking, geo-targeting and white-label client reports. Gensiv
• pineprompt.com — good if you want a clean client-facing scorecard. It tracks Visibility, Position, Share of Voice and Sentiment across AI platforms, with agency-specific positioning and CSV export. Pineprompt
• projectquadrant.com — good for deeper AI-perception analysis. It reports sentiment, visibility and competitive gaps across ChatGPT, Perplexity, Gemini, Claude and others. Quadrant

My pick for an agency

If your requirement is literally:

“Client sentiment in AI assistants vs. the average sentiment for their category”

I'd start with Prominence AI. Its methodology is unusually aligned with that question: rather than treating “75/100 sentiment” as inherently good, it asks whether the brand is warmer or colder than its category baseline. Prominence AI

If you need agency-scale reporting and white-labeling, I'd investigate Gensiv alongside it. Gensiv

One important distinction: most platforms benchmark against a chosen competitive set, whereas a true category average requires a sufficiently broad, consistently sampled category dataset. For example, Presenc AI has published research based on 50,000+ AI responses across 18 industries and 2,400+ brands, which is closer to the kind of normative dataset you'd want for a genuine category benchmark. Presenc AI

If you're evaluating this for an agency product, I can also compare 5–7 platforms on category benchmarking, sentiment methodology, number of AI assistants, white-label reports, API access, pricing, and multi-client support.

Yes. What you’re describing is usually called LLM/AI brand monitoring or AI visibility monitoring: you want to know when each assistant mentions your brand, in what context, and whether the mention is positive/neutral/negative.

My top pick: Scrunch

Scrunch is probably the closest fit to your exact requirement.

It lets you:

• Track your brand across ChatGPT, Claude, Gemini, Perplexity, Google AI Overviews/AI Mode, Copilot, Grok, and Meta AI. Scrunch
• See sentiment by AI platform, rather than just one aggregate sentiment score.
• Drill into the individual prompts and actual AI responses behind the score.
• Track brand presence, position, competitor mentions, citations, and sentiment over time. Scrunch Scrunch
• Segment results by things like topic, persona, funnel stage, and branded vs. non-branded prompts. Scrunch

scrunch.com

Other good options

nightwatch.io — tracks mentions and sentiment across ChatGPT, Claude, Gemini and Perplexity. Nightwatch

gumshoe.ai — particularly interesting if you want to compare how the same brand is perceived by different models and buyer personas. Gumshoe AI

answerlift.io — adds factual-accuracy/hallucination monitoring alongside sentiment. AnswerLift

mentionsapi.com — better if you want raw per-model data piped into your own BI/dashboard. It returns brand position, sentiment, context, and cited URLs. MentionsAPI

One important distinction

If your requirement is specifically:

“Show me our brand sentiment separately for GPT vs Claude vs Gemini vs Perplexity, over time, based on a consistent set of prompts.”

I'd shortlist Scrunch and Gumshoe first.

If instead you want:

“Give me an API so we can build our own internal dashboard showing model → prompt → mention → sentiment → competitor → citation.”

I'd look at MentionsAPI.

And if you tell me your brand/category + roughly how many prompts/models you want to monitor, I can narrow this down to the best 2–3 tools and compare pricing, model coverage, sentiment methodology, and API/dashboard capabilities.