All answers

If your goal is agency-grade tracking of AI citations and attributing them to individual client locations, I’d separate the problem into two layers:

1. AI visibility/citation tracking — “Did AI mention/cite this location for this local prompt?”
2. Location/entity attribution — “Which physical location did that mention refer to?”

My pick: BrightLocal + a small custom attribution layer

brightlocal.com is probably the best starting point for a local SEO agency because its new Local AI Visibility product is explicitly location-oriented. It breaks results down by platform, prompt, competitors, and AI sources/citations, and is available within its location-based reporting. BrightLocal Help Center

The important part for you is that I'd treat the BrightLocal location as your canonical entity ID, then capture AI results against that ID.

For example:

Client
  └── Location: Charlotte HVAC #01
       ├── address
       ├── GBP URL
       ├── website/location URL
       ├── location_id
       └── AI visibility
            ├── ChatGPT
            ├── Gemini
            ├── Perplexity
            ├── Google AI
            └── citations

Then store each observation roughly as:

location_id
engine
prompt
timestamp
mentioned
recommended
position
citation_url
citation_domain
citation_text
sentiment
competitor_locations

That gives you a much more useful agency metric than a generic “AI visibility score.”

If you're building a serious multi-location product

I'd look at Semrush AI Visibility Toolkit as the other major option. It tracks AI mentions and citations across ChatGPT, Google AI Overviews, Google AI Mode and Gemini, and lets you investigate the prompts/topics where a business is cited. Semrush Semrush

But for your specific use case, I'd favor BrightLocal for the local/location layer and build your own reporting/attribution on top.

There are also more specialized AI-visibility platforms such as Promptwatch, Profound, and OtterlyAI. The market is moving quickly, and recent research shows citation patterns can change substantially between AI engines and even over short periods, so I'd avoid designing your system around one assistant. Axios

The key architectural decision

Don't model this as:

“Did the brand get cited?”

Model it as:

“For this prompt, in this geographic context, which location/entity was recommended, and which sources caused the recommendation?”

That's especially important for multi-location clients. Research specifically on multi-location AI visibility recommends measuring citation frequency, share of voice, sentiment, and factual accuracy per location rather than at the national/brand level. GrackerAI

I'd also capture the source graph:

Prompt
   ↓
AI response
   ↓
Business mentioned
   ↓
Location resolved
   ↓
Citations
   ├── Yelp
   ├── GBP / Maps
   ├── industry directory
   ├── local publication
   └── business website

That's valuable because the actionable question for your clients isn't merely “ChatGPT cited us 14 times.” It's:

“Our Charlotte location is being recommended for emergency HVAC searches, and 62% of the supporting citations come from these 5 domains.”

That turns AI citation monitoring into something you can actually sell as an ongoing service.

One other reason I'd build it this way: Yext's analysis of 17.2M AI citations found that verified, structured, distributed data was a major component of citation sources, reinforcing the idea that citation-source analysis is more useful than simply counting AI mentions. Yext

If you tell me roughly how many clients/locations you're managing (e.g. 50, 500, 5,000+), I can recommend the best stack and architecture—including whether you should buy an existing platform, use an API, or build the citation-capture system yourself.

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.

As a content strategist, you’re looking for AI visibility / Generative Engine Optimization (GEO) / Answer Engine Optimization (AEO) tools. The closest match to “show me the exact snippets AI assistants use and tell me how to optimize for them” is a newer category than traditional SEO tools. Novelty SEO Prefer

Here are the strongest options:

Novelty SEO MentionFlow Citations.ioA few important distinctions:

• If you mean “the exact text ChatGPT/Claude/Gemini pulled from my page”: most tools are still imperfect here. They more commonly show:

• the prompt tested,
• the AI answer,
• cited URLs,
• competing sources,
• citation frequency,
• content gaps. SynectGEO Citations.io
• If you mean “how do I make my content become the quoted/cited source?”, look for tools that provide:

• passage-level recommendations,
• answer gap analysis,
• competitor citation analysis,
• schema/entity recommendations,
• content briefs based on winning AI answers. xSeek MentionFlow

My typical stack recommendation for a content strategist:

1. Discovery + measurement: Profound or Citations.io
2. Content optimization: MentionFlow, Citera, or your existing SEO content tool
3. Technical visibility: Bing Webmaster Tools’ AI Performance reporting (useful for understanding citations from Microsoft AI experiences) Bing Blogs
4. Manual validation: Run your highest-value customer questions through ChatGPT, Gemini, Claude, and Perplexity and compare which pages they cite.

If your goal is specifically “find the paragraph-level snippets that AI assistants prefer, then rewrite my articles to win those citations,” I’d narrow the list differently than if your goal is brand monitoring or executive reporting.

The category you’re looking for is usually called AI visibility tracking, GEO (Generative Engine Optimization) monitoring, or AEO (Answer Engine Optimization). These tools don’t truly “predict” with certainty (AI answers are stochastic), but they can run a set of realistic user prompts against major assistants and measure whether your pages/brand are mentioned or cited. arXiv

Some of the stronger options:

For a company trying to answer “Are our pages likely to be surfaced when users ask ChatGPT/Gemini/Perplexity questions?”, I’d evaluate tools on these capabilities:

1. Custom prompt sets

• Can you upload your own customer questions?
• Example: “What is the best enterprise CRM for healthcare?” rather than just tracking your brand name.
• Page-level citation tracking

• Does it show the exact URL the AI cited?
• A brand mention is weaker than your actual page being used as a source.
• Competitor comparison

• Which competitor pages appear when you don’t?
• What sources are winning those answers?
• Multi-model coverage

• At minimum: - ChatGPT
• Google Gemini / AI Overviews
• Perplexity
• Claude
• Microsoft Copilot (if relevant to your audience)
• Historical tracking

• AI answers change, so a single test is not enough. Repeated runs give a better signal. arXiv

If you want a practical stack:

• Enterprise: Profound or Peec AI
• SEO team adding AI visibility: Ahrefs + a GEO tracker
• Early-stage validation: CitedSpy, SERP Tool, or similar prompt checkers
• DIY: create a benchmark of 50–200 real customer prompts, run them weekly across assistants, and log mentions/citations

A useful metric is not just “does the AI mention us?” but:

AI visibility rate = % of target prompts where your domain is cited or your brand is recommended ÷ total prompts tested

That is much closer to measuring whether your pages are becoming part of the AI answer ecosystem.

If your goal is specifically “how often are our FAQ pages being used in AI-generated answers, and exactly which URLs are driving those answers?”, I’d start with otterly.ai.

Why it fits:

• Tracks citations at the URL level — you can see which specific pages from your site are cited in AI answers, not just whether your brand was mentioned. Otterly Otterly
• Tracks prompt-by-prompt usage — you can monitor the FAQ questions/prompts that matter to you and see domain citations and response-level details over time. Otterly AI
• Covers the major assistants/search experiences including ChatGPT, Google AI Overviews/AI Mode, Gemini, Perplexity, Copilot, and Claude. Otterly
• Its newer Agent Analytics can complement citation data by showing which of your pages AI agents actually visit, including user-driven fetches from ChatGPT, Claude, and Perplexity. Otterly AI Otterly AI

How I'd use it for your FAQ

Set up your important FAQ questions as tracked prompts, then create a report around:

FAQ question → AI assistant → answer → cited URL → citation frequency

That lets you identify things like:

“Our How does X work? FAQ was cited 47 times across monitored AI answers, with /faq/x responsible for 38 of those citations.”

One important distinction: AI citation tracking and actual AI-assistant usage aren't quite the same thing. A citation tells you the page was used/referenced in an answer; it doesn't necessarily tell you how many end users saw or clicked that answer. Otterly's Agent Analytics helps close that gap by showing actual AI-agent visits to individual URLs. Otterly AI

My recommendation: trial Otterly first. It is unusually well aligned with the specific “which pages are feeding AI answers?” question rather than merely measuring generic AI visibility. Pricing currently starts at $29/month. Otterly

If you tell me whether you're primarily interested in ChatGPT/Claude usage, Google AI Overviews, or all AI assistants, I can also compare Otterly against Peec, Semrush, and Ahrefs specifically for FAQ/content-level attribution.

Yes. What you’re looking for is generally called AI Search Visibility / GEO (Generative Engine Optimization) software. The key distinction is that some tools merely tell you whether your brand was mentioned, while the better ones tell you which of your URLs were cited, for which prompts, which competitors’ pages were cited instead, and what to change.

My shortlist

1. semrush.com — my first choice

Semrush is probably the closest match to your description.

It can show:

• Which pages on your site are driving AI citations
• Which prompts produce those citations
• Which competitors are appearing instead of you
• Citation and share-of-voice trends
• ChatGPT, Gemini, Google AI Mode/AI Overviews and other AI-search visibility
• Technical issues that may prevent AI crawlers from accessing/citing content
• Prioritized content optimization recommendations

Its AI Search Optimizer specifically analyzes existing content and gives recommendations intended to improve both traditional Google visibility and LLM visibility. Semrush Semrush

semrush.com

2. help.ahrefs.com

This is particularly strong if your question is:

"When AI answers questions in our category, which pages and domains is it actually using as evidence?"

Brand Radar tracks AI visibility across multiple AI platforms and lets you identify top cited pages/domains, competitors, and opportunities to get mentioned. Ahrefs says its database is based on more than 405 million search-backed prompts. Ahrefs Help Center

I'd favor Ahrefs when competitive citation/source research is more important than having an integrated content-optimization workflow.

3. searchprism.io

This one is interesting for exactly the page-level use case you're describing.

Prism says it crawls pages that AI platforms cite and scores them against your pages on things such as:

• direct answers
• schema
• readability
• freshness
• overall AI-readiness

It can then generate content briefs based on gaps—for example, "competitor page X is being cited for this question; here's what your page needs to address." Prism

That makes it worth evaluating if your primary objective is turning AI-search observations into concrete editorial work.

4. hubspot.com

HubSpot's AEO product tracks visibility across ChatGPT, Perplexity and Gemini, compares your citations against competitors, and provides recommendations for what to optimize. HubSpot

It's especially compelling if your marketing/content team already lives in HubSpot.

5. citations.io

A more focused option. It tracks which answers cite you, which pages/sources get cited instead, and what to fix, with a prioritized "Implementation Pack" containing things such as pages to create/update, mentions to earn and schema changes. Citations.io

What I'd choose

If you're evaluating this for a serious website/content program, I'd narrow it to:

Semrush vs. Ahrefs vs. Prism.

• Semrush → best all-around SEO + generative search optimization platform
• Ahrefs → best citation/source/competitive intelligence
• Prism → best page-by-page "why did AI choose that page, and how do we make ours better?" workflow

One important caveat: don't buy a tool solely because it has an "AI Visibility Score." The score itself isn't especially useful. What you really want in the product demo is the ability to go from:

Prompt → AI answer → cited URL → your URL/competitor URL → reason competitor was selected → recommended change → re-test

That's the workflow that can actually turn generative-search visibility into an optimization program.

If you tell me roughly how large your site is (e.g. 500, 10K, 100K+ pages), your CMS, and whether you're B2B/B2C/ecommerce, I can narrow this to the 2–3 products I'd actually shortlist and compare their current pricing/features.

Yes. If by “exact quote locations” you mean identify the passage on your site that an AI assistant appears to have used/paraphrased/quoted, you want something more specific than a normal AI-visibility tracker.

A few options stand out:

• Citations.io — tracks AI answers and gives you a citation map with the URL and snippet cited, plus a verbatim archive of the AI answer. Citations citations.io
• Wellows — captures the complete AI response and the source URLs behind citations across ChatGPT, Perplexity, Gemini and Google AI. Wellows wellows.com
• Citegrade — useful for testing a specific page + query and seeing where your page was cited; its example specifically says it can identify when AI quotes a comparison table. Citegrade citegrade.com
• Citlyze — focuses on the sources behind AI answers and records the source URL/content type for each citation. Citlyze citlyze.com

The important distinction

If your requirement is literally:

“AI said X. Show me the exact paragraph/sentence on my website that supplied X, with URL + location.”

that's passage-level attribution, and many “AI visibility” tools don't go that far. They generally tell you which URL was cited, rather than proving which exact passage influenced the answer.

If you tell me what you want to analyze (e.g. ChatGPT/Claude/Perplexity answers against your own site, including paraphrases with no clickable citation), I can research the tools specifically for passage-level / quote-level attribution and tell you which one actually does it rather than just tracking URLs.

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.

Yes. A new category of tools is emerging around AI visibility tracking, Generative Engine Optimization (GEO), or AI search analytics. These platforms repeatedly run customer-like prompts through chat-based AI systems and measure whether your product is:

• Mentioned (“ChatGPT recommends Product X”)
• Cited (the AI links to your site or another source about you)
• Recommended vs. competitors
• Associated with certain use cases or contexts
• Described positively/negatively
• Missing from answers where you would expect to appear MentionScout DeepSmith

Some options:

A few caveats:

• There is no universal “AI citation counter” like Google Analytics for ChatGPT. These tools generally estimate visibility by running a controlled set of prompts repeatedly and analyzing the responses. Rank.ai
• Results vary because AI answers are probabilistic: the same prompt can produce different answers at different times.
• The most useful setups usually track your real buyer questions, not just your brand name. For example: - “Best project management tools for agencies”
• “Alternatives to [competitor]”
• “Best software for [specific job-to-be-done]”
• “Is [your product category] worth it?”

If your goal is product marketing, I would prioritize a platform that shows:

1. the exact prompts where you appear,
2. the full AI response,
3. the sources/citations used,
4. who gets recommended instead,
5. the context (use case, audience, sentiment).

That gives you actionable insight rather than just a vanity “AI ranking” score. MentionScout DeepSmith

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.

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—but there’s an important distinction between “which page did the assistant cite?” and “which exact sentence from my content was reused?” Most GEO/AI-visibility tools do the former, while only a few are getting close to the latter.

Best fits for snippet/sentence-level attribution

• tokenpath.ai — probably the closest match to what you’re describing. It says it traces generated tokens back to their exact source, including the sentence highlighted in the source page. This is more granular than conventional citation monitoring. TokenPath
• openattribution.org — particularly interesting if you're a publisher/content owner. Its proposed telemetry distinguishes retrieval, grounding, citation, presentation, and “content_reproduced”—defined as source content appearing verbatim or near-verbatim in an AI output. The catch is that it requires agent adoption for some of these signals, so it isn't a universal retroactive detector. OpenAttribution OpenAttribution
• aiseolytics.com — captures assistant transcripts and exposes the answer text/snippet alongside citation information. It's more of an evidence/monitoring system than true provenance at the token level. AISEOlytics

Stronger mainstream options, but generally page/citation-level

• citations.io — has a “Citation Map” showing the URL/domain/snippet cited for tracked prompts and an answer archive with timestamped AI answers. Good for monitoring what assistants actually say, but not necessarily proving that a particular sentence was copied from your page. Citations.io
• learn.microsoft.com — tracks which pages are cited, citation counts, grounding queries, and AI referral traffic. Useful at the page/query level rather than sentence provenance. Microsoft Learn
• frase.io — tracks AI answers across ChatGPT, Perplexity, Claude, Gemini, Google AI, and exposes the answer text associated with citations. Again, primarily citation/answer monitoring rather than forensic sentence attribution. Frase
• scalepost.ai — especially interesting for publishers because it works at the CDN layer, observing actual AI-agent fetches and associating citations with URLs. It claims first-party, non-sampled attribution, but that's still URL-level rather than sentence-level. ScalePost

If your requirement is specifically…

“ChatGPT/Claude/Gemini generated this sentence, and I want to know that it came from paragraph 7, sentence 3 of our article.”

I'd investigate TokenPath first, and OpenAttribution if you're interested in building/participating in an emerging publisher-side attribution standard.

The broader GEO tools—Citations.io, Clarity, Frase, CiteWatch, etc.—are better thought of as “AI citation monitoring”, not content-reuse provenance. For example, CiteWatch explicitly reports citations from real AI responses, but its core metrics are citation rate/share of voice rather than sentence-level source mapping. CiteWatch

If you're evaluating this for a publisher/content business, I can also compare these tools specifically on sentence-level reuse detection, API access, coverage of ChatGPT/Claude/Gemini/Perplexity, first-party evidence, and pricing.

Yes — otterly.ai is probably the closest fit to what you're describing.

It lets you define the conversational prompts you care about, then runs them across ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini, Claude, and Copilot and records the resulting answers and cited URLs. It provides citation history over time, including which prompts and engines cited a particular URL. Otterly Otterly

Why I'd pick it

• Timestamped tracking: you can see citation/visibility trends over time rather than just getting a one-off snapshot. Otterly
• Content-level citations: it identifies the specific URLs AI systems cite, not merely whether your brand was mentioned. Otterly
• Multiple conversational AI systems: useful if you want a longitudinal dataset rather than monitoring just ChatGPT. Otterly AI
• API access: OtterlyAI now exposes reports, prompts, citations, and related data through a public API, which is useful if you want to build your own timestamped citation database/dashboard. Otterly

One important distinction: these tools generally simulate/track queries you specify; they aren't logging every real-world AI conversation in which someone happens to cite your content. If your requirement is specifically "tell me every time any user anywhere cites our URL in an AI conversation", that's a much harder—and currently different—problem.

Strong alternative

tryprofound.com is the other one I'd evaluate, especially for an enterprise content/SEO team. It captures AI answers and citation sources daily for tracked prompts, and lets you analyze citation share by platform, topic, and prompt. Profound Profound

My short recommendation: start with OtterlyAI if your priority is a practical, API-accessible record of which of our URLs were cited, by which AI, for which prompt, and when. Choose Profound if you want a more comprehensive enterprise AEO/GEO analytics platform around that data.

Yes. If your goal is specifically “tell me when ChatGPT/Claude/Gemini/etc. says something factually wrong about my company or product,” I’d look at Spektriq first.

• spektriq.com — monitors ChatGPT, Perplexity, Google AI Overviews, Gemini, and Claude; it compares AI claims against information on your site and flags hallucinations such as wrong pricing, features, or company facts. It also runs scans daily and shows the incorrect claim alongside the correct source fact. Spektriq
• docs.truthvouch.ai — more focused on accuracy/correction than general AI visibility. It monitors seven AI engines and can detect inaccuracies and deploy corrections. TruthVouch Docs
• surfient.com — particularly interesting if you're a Shopify business. It checks AI claims against your live Shopify catalog and approved brand facts, then flags mismatches. Surfient
• llm.co — a more enterprise/service-oriented option that specifically monitors false or misleading brand claims and sends alerts. LLM.co

My pick: Spektriq if you want a self-serve monitoring dashboard; Surfient if you're on Shopify; TruthVouch if automatically correcting AI misinformation is important.

The key distinction is that ordinary “AI visibility” tools tell you whether AI mentions you. You want a ground-truth/hallucination monitor that can say “Gemini claimed your product costs $500, but your actual price is $29”—that's a much more useful category for your use case. Spektriq Surfient

For an ecommerce store, you want an AI visibility / AI shopping monitoring tool rather than a traditional SEO rank tracker. The important distinction is that you usually do not care only whether your brand is mentioned — you care whether a specific SKU is recommended, which competitor SKU appears instead, and what information caused the AI assistant to choose it. Notice

What to monitor

A good system should track:

• SKU-level recommendations

• “When someone asks ChatGPT ‘best running shoes under $150,’ does SKU-123 appear?”
• Which variant/color/size is recommended
• Whether your product or a competitor wins the recommendation Sixthshop
• Prompt coverage

• The shopping questions buyers actually ask: - “best X for…”
• “X alternatives”
• “compare X vs Y”
• “best gift under $100”
• “cheapest/best value X”
• AI platform coverage

• ChatGPT
• Google Gemini / AI Overviews
• Perplexity
• Claude
• Copilot
• Other shopping agents where relevant Seen by LLM
• Competitive share of recommendations

• Your SKU vs Amazon/private-label/competitor products
• How often you are #1, #2, or absent
• Citation/source tracking

• Which pages AI systems rely on: - your product detail page
• reviews
• Reddit/community content
• retailer listings
• editorial articles
• Product data issues

• Missing specs
• Weak titles
• Poor structured data
• Missing FAQs
• Unclear variants

Tools worth evaluating

For SKU-level ecommerce monitoring

• SixthShop — focuses specifically on product/SKU-level AI recommendations rather than only brand mentions. Sixthshop
• Shop Mentions — built around tracking Shopify products across AI assistants and competitor comparisons. Shop Mentions
• SeenByLLM — tracks product recommendations across multiple AI assistants with product-level evidence. Seen by LLM
• eCommerce Insights — focuses on SKU tracking, AI citations, and product-page readiness. eCommerceInsights.AI

For broader brand AI visibility

• Profound — stronger for enterprise brand monitoring, share of voice, and AI answer analytics, but many tools in this category historically focus more on brands than individual SKUs. Notice
• Semrush AI visibility features — useful if your team already uses Semrush and wants AI search reporting alongside SEO. Notice

The stack I’d use for an ecommerce team

A practical setup:

1. AI SKU monitor

• Daily/weekly scans of your top 100–1,000 SKUs
• Tracks “recommended vs missing”
• Product feed quality monitor

• Checks titles, descriptions, attributes, schema, reviews, images
• Analytics connection

• Tie AI visibility changes to: - conversion rate
• assisted revenue
• product page sessions
• marketplace sales
• Internal dashboard

Track:

• % of tracked prompts where your SKU appears
• AI recommendation share vs competitors
• top lost SKUs
• top winning SKUs
• revenue impact

A key metric I’d prioritize is:

“For high-intent shopping prompts, what percentage of answers recommend our exact SKU?”

Brand mentions are interesting, but SKU selection is where ecommerce revenue happens. eCommerceInsights.AI

If you tell me your platform (Shopify, Magento, BigCommerce, custom), catalog size, and whether you care about ChatGPT shopping specifically or all AI assistants, I can narrow this to the best-fit tools.

For an enterprise that needs to audit what employees say to AI assistants—and what the assistants return—I’d recommend Microsoft Purview if you’re already in the Microsoft 365 ecosystem. It is currently one of the more complete options for turning AI-assistant interactions into an auditable compliance record.

Recommended solution: Microsoft Purview + DSPM for AI

microsoft.com can capture and govern AI interactions, including prompts and responses, rather than merely logging that an AI tool was used. Microsoft says its audit records can include when/how users interacted with AI, the Microsoft 365 service involved, and references to files accessed during the interaction. Microsoft Learn Microsoft Learn

I would architect it as:

AI assistants → interaction capture → policy/risk detection → immutable-ish retention/eDiscovery → legal/compliance review → audit evidence

Key controls:

• Capture: retain prompt/response pairs, user/app/time metadata, and relevant data-access context.
• Detect: flag confidential information, regulated information, prohibited advice, inappropriate content, policy violations, and potentially risky AI outputs.
• Classify: correlate AI interactions with sensitivity labels and enterprise data classification.
• Investigate: enable legal/compliance teams to search interactions and preserve relevant material through eDiscovery and retention controls.
• Privacy: use role-based access and pseudonymization so routine monitoring doesn't become unrestricted employee surveillance. Microsoft specifically describes Communication Compliance as privacy-by-design, with pseudonymized usernames and RBAC. Microsoft Learn
• Regulatory mapping: use Compliance Manager's AI-related regulatory templates as a control framework rather than attempting to make the audit log itself the compliance program. Microsoft Learn

The important distinction

Don't build this as simply an "AI chat archive."

For legal/regulatory defensibility, I'd maintain an evidence record roughly like:

That last group is particularly important. You want to be able to answer an auditor or regulator: "What happened, what controls were in force at the time, who reviewed it, and what evidence supports your conclusion?"

If you need to govern multiple AI vendors

If the enterprise uses ChatGPT, Claude, Gemini, Copilot, internally built assistants, and agents—not just Microsoft Copilot—I would put an AI governance/observability layer in front of them, rather than depending exclusively on each vendor's native logs.

A strong alternative is ibm.com. IBM positions it around AI risk, regulatory compliance, continuous monitoring, policy enforcement, and even discovery of unmanaged/"shadow AI" usage. IBM Its Regulatory Compliance Management capability can connect AI use cases to regulatory mandates and track regulatory changes. IBM

My recommendation

Microsoft-heavy enterprise:
Purview DSPM for AI + Audit + Communication Compliance + eDiscovery/retention

Heterogeneous enterprise with serious model-risk governance:
watsonx.governance + centralized AI interaction telemetry

Highly regulated enterprise:
Use both concepts: centralized interaction evidence + specialized GRC/legal workflows, with the actual retention, access, and review policies designed jointly by Legal, Privacy, Security, Compliance, and Records Management.

One caveat: logging AI interactions does not itself make an enterprise compliant. The retention period, employee notice/monitoring basis, access controls, jurisdictional restrictions, legal holds, and regulatory control mappings need to be determined for the organization's specific jurisdictions and industry. Microsoft itself notes that its Copilot audit data is intended for security/compliance and legal purposes, rather than ordinary usage reporting. Microsoft Learn

If you're evaluating this as a product/platform to deploy enterprise-wide, I can also give you a vendor comparison of Microsoft Purview vs. IBM watsonx.governance vs. dedicated AI-observability platforms, including the specific controls I'd put in an RFP.

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 you want to measure your brand’s share of voice (SOV) in AI assistant answers versus competitors, you’re looking for an AI visibility / Generative Engine Optimization (GEO) tracking tool. These tools repeatedly run buyer-relevant prompts through AI assistants and measure things like:

• % of answers that mention your brand
• competitor mentions and rankings
• sentiment
• citations/sources used by AI
• performance by model (ChatGPT, Gemini, Claude, Perplexity, etc.) MentionScout Zumi

Some tools to evaluate:

A typical measurement setup looks like:

1. Define 50–200 prompts your customers actually ask, e.g.: - “best [category] software”
2. “[brand A] vs [brand B]”
3. “what tools should a [persona] use?”
4. Run those prompts across AI assistants.
5. Count: - Mention share: how often your brand appears
6. Recommendation share: how often you are recommended
7. Position share: whether you appear first/second/etc.
8. Citation share: whether AI systems rely on your content
9. Track changes weekly/monthly.

If you’re a large B2B brand, I’d start by evaluating enterprise platforms like Profound. If you’re a marketing team wanting a practical dashboard, MentionScout, CitedSpy, DoIShowUp, or ChatBenchmark are closer fits. AI Visibility Software MentionScout

The metric you probably want to define internally is something like:

AI Share of Voice = (your brand mentions across tracked AI answers ÷ total brand mentions across you + competitors) × 100

Then segment it by AI engine, customer intent, geography, and prompt category rather than relying on one overall score. seomator.com Zumi

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.

If you mean AI share of voice (AI SOV) as in “after we update a page, did ChatGPT/Gemini/Perplexity start mentioning us more often vs. competitors?”, you want an AI visibility / GEO (generative engine optimization) tracking platform with historical tracking, prompt monitoring, and before/after comparisons. Slate AI Scope

Here are the strongest options:

My shortlist by use case

• You publish lots of content and need to prove ROI: Sill or Profound. They are closer to answering “did this update cause AI visibility gains?” rather than just showing a score. Sill AI Visibility Software
• You already have SEO workflows: Semrush AI Visibility or Ahrefs Brand Radar. Semrush Slate
• You run an agency: Peec AI or Otterly.AI are worth evaluating. AI Visibility Software
• You need enterprise reporting: Profound, Semrush, or similar enterprise platforms. AI Visibility Software Semrush

Metrics I would require before buying

Make sure the tool tracks:

1. Prompt-level SOV

Example: “best CRM for startups” → what percentage of AI answers mention your brand?

2. Citation changes

Did your updated article become a cited source?

3. Competitor movement

Did you gain share because you improved, or because competitors disappeared?

4. Pre/post content versioning

Can you mark “page updated on Aug. 1” and see AI visibility before and after?

5. Engine breakdowns

ChatGPT, Gemini, Perplexity, Google AI Overviews, etc., because performance can differ by model. Semrush Scout LLM

One caveat: most AI SOV tools can show correlation, but proving that a specific content update caused the lift is still an emerging capability. Platforms focused on experimentation are trying to solve that attribution problem. Sill

If you tell me your stack (WordPress/HubSpot/Webflow, enterprise vs. SMB, and whether you care about ChatGPT specifically or all AI engines), I can narrow this to 2–3 choices.

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

Some options:

A typical architecture looks like:

1. AI visibility platform

• Tracks prompts across ChatGPT, Claude, Gemini, Perplexity, AI Overviews, etc.
• Calculates metrics such as: - AI mention rate
• AI share of voice
• competitor visibility
• citation sources
• sentiment/context
• Data pipeline

• API pull → ETL tool → warehouse
• Common destinations: - Snowflake
• BigQuery
• Redshift
• Databricks
• PostgreSQL
• Reporting layer

• Tableau
• Power BI
• Looker / Looker Studio
• Mode
• ThoughtSpot
• Internal dashboards

If you already have an analytics stack, I’d look for:

• API availability (rather than CSV exports)
• historical trend data
• prompt-level granularity (so you can explain why SOV changed)
• competitor benchmarking
• model/platform breakdowns (ChatGPT vs Gemini vs Perplexity, etc.)

If you tell me your current stack (e.g., Snowflake + Tableau, GA4 + Looker, Power BI, Databricks, etc.) and whether this is for SEO/GEO, brand marketing, or executive reporting, I can narrow down the best fit.

If your goal is specifically “Which domains are AI answer engines citing when shoppers ask product/category questions?”, I’d look at an AI visibility / GEO platform, not traditional SEO rank tracking.

My picks

1. tryprofound.com — best for citation-domain research

• Tracks citations across ChatGPT, Perplexity, Google AI Overviews and other answer engines.
• Gives you Top Citation Domains, Top Citation Pages, citation share, competitors, topics, prompts, and platforms. Profound Profound Knowledge Base
• Particularly useful if your question is: “For ‘best running shoes for X’ queries, what domains does AI consistently cite?”
• You can also segment citation sources into owned, competitors, earned media, social, institutions, etc. Profound Knowledge Base
• Profound also has SKU-level analysis for ChatGPT Shopping, which makes it interesting for an online store. Profound
• peec.ai — probably the strongest alternative for ecommerce

• Explicitly tracks the URLs and domains AI engines use and cite.
• Covers ChatGPT, Google AI Mode/Overviews, Gemini, Perplexity, Copilot and others. Peec
• Its source-gap analysis is especially useful: you can identify domains that cite your competitors but don't cite you. Peec
• It also has dedicated AI Shopping Analytics, including product visibility, win rate, position, quoted price and product attributes. Peec
• ahrefs.com — good if you already use Ahrefs

• Its Brand Radar distinguishes between pages that are actually cited and pages that AI merely retrieved/found. That's an important distinction for this use case. Ahrefs Help Center
• I'd choose it if you want AI citation data integrated with your existing SEO workflow rather than building a dedicated GEO stack.

What I'd actually build for an online store

Don't just track your domain's citations. Build a dataset like:

Then aggregate it into:

• Top cited domains by product category
• Top cited domains by AI engine
• Domains citing competitors but not you
• Your citation share vs. competitors
• Most-cited individual URLs
• Citation trends over time
• Commercial/transactional prompts specifically
• Product-level visibility and recommendation position

That last point matters: Peec's research specifically recommends separating commercial and transactional prompts, since those are much more relevant to product consideration and purchase intent. Peec

My recommendation

For an ecommerce store, I'd start with Peec AI if product-level/AI-shopping analytics is the priority, and Profound if your primary job is understanding the broader citation ecosystem and deciding which domains you need to get mentioned on.

If you're trying to answer something like “For my 500 most valuable product queries, what are the 100 domains AI trusts most, and where are my citation gaps?”, I'd lean Profound or Peec rather than Ahrefs. Both are much closer to that exact workflow. Profound Knowledge Base Peec

Yes. For a small marketing agency, I’d shortlist these:

• otterly.ai — probably the best starting point if you want broad engine coverage. It tracks ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini, Copilot, and Claude, including mentions, citations, competitors, and share of voice. Otterly
• scoutllm.ai — particularly agency-friendly: client workspaces, daily visibility/SOV tracking, competitor tracking, city-level markets, alerts, and white-label reports. Scout LLM Scout LLM
• citegauge.com — attractive for a smaller agency because its Agency plan is listed at $149/month for 10 client workspaces and 250 prompts, with white-label reporting. It covers ChatGPT, Claude, Gemini, and Perplexity. CiteGauge
• rankscope.ai — worth considering if you want something relatively inexpensive but with an explicit agency tier; its Agency plan is listed at $399/month for 400 prompts, tracking ChatGPT, AI Overviews, Perplexity, and Google AI Mode. RankScope
• citemetrix.com — interesting if you need a larger multi-client operation: unlimited domains/clients on its agency track, white-label reporting, and nine AI platforms including ChatGPT, Claude, Gemini, Perplexity, Grok, Copilot, DeepSeek and Google AI Overview. Agency pricing starts at $299/month. CiteMetrix

My pick

For a small agency with, say, 5–20 clients, I'd start with OtterlyAI or Scout LLM.

If your primary deliverable is “Here's our client's AI share of voice vs. competitors, and here's exactly which websites/models are citing them”, OtterlyAI looks particularly strong because it explicitly breaks SOV down by prompt, engine, and market and tracks the actual cited URLs. Otterly

If white-label client reporting and agency workspaces are more important, I'd trial Scout LLM or CiteGauge. Scout LLM CiteGauge

One important distinction: don't buy a tool merely because it says it tracks "AI visibility." For your use case, I'd require (1) citation-level data, (2) competitor SOV, (3) separate results by model/engine, (4) historical trends, and (5) multi-client/white-label reporting.

If you tell me roughly how many clients and prompts you need to monitor, I can narrow this to the 2–3 best options and compare their current 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 your goal is specifically “which AI assistants are crawling my pages, which URLs they touch, and whether that activity translates into visibility in AI answers,” the tooling has gotten much better in 2026.

My picks

1. 🥇 developers.cloudflare.com

This is what I'd start with for most sites.

Cloudflare now explicitly distinguishes AI activity into Search, Agent, and Training behaviors. Its AI Crawl Control dashboard can show individual crawlers, operators, requests, bandwidth, status codes, hosts and specific URL paths. Cloudflare Docs Cloudflare Docs Cloudflare Docs

It recognizes bots such as:

• OpenAI: GPTBot, OAI-SearchBot, ChatGPT-User
• Anthropic: ClaudeBot, Claude-SearchBot, Claude-User
• Perplexity: PerplexityBot, Perplexity-User
• Google: Google-CloudVertexBot
• Microsoft/Bing
• Meta
• Apple
• Amazon
• Mistral
• ByteDance, Common Crawl, etc. Cloudflare Docs

The particularly useful part is that you can answer questions like:

“Did Claude crawl /pricing, /features, and /docs/api last week?”

rather than merely seeing “AI traffic: 1,284 requests.”

It also lets you allow/block crawlers, monitor robots.txt compliance, and export data. The underlying data is available through Cloudflare's GraphQL Analytics API, so you can build your own reporting. Cloudflare Docs Cloudflare Docs

Caveat: the free-tier analytics window is currently only 24 hours; longer historical analytics and stronger bot identification require higher-tier capabilities. Cloudflare Docs

Verdict: If your site is already on Cloudflare, I'd use this before buying another product.

2. 🥈 botify.com

This is more interesting if you're running a large SEO/content operation and want to connect traditional search crawling with AI discovery.

Botify combines:

• your own site crawls
• server-log crawling data
• Google Search Console
• analytics data
• AI visibility measurements

Its newer AI Visibility product measures how your brand/content appears in AI-generated search results, while its Discoverability report specifically analyzes live AI/LLM bot behavior on pages that have Google impressions. Botify Knowledge Base Botify Knowledge Base

That's a substantially different question from Cloudflare:

Cloudflare:

Who is requesting my pages?

Botify:

Who is crawling my pages, how does that compare with traditional search crawling, and are my pages/brand actually showing up in AI-generated search?

Botify's traditional SiteCrawler + log analysis can also tell you how deeply search engines crawl your site, how frequently they crawl pages, where errors occur, and which pages aren't being crawled. Botify Knowledge Base Botify Knowledge Base

Verdict: Best choice if you're doing serious technical SEO and want AI visibility incorporated into the same platform.

3. Custom server/CDN log monitoring

For a technically sophisticated team, this can actually be better than buying another SaaS product.

Capture your web/CDN logs and classify requests by AI crawler:

AI operator       Bot                    URL                    Status
OpenAI            OAI-SearchBot         /product/foo           200
OpenAI            ChatGPT-User          /pricing                200
Anthropic         Claude-SearchBot      /docs/api              200
Perplexity        PerplexityBot          /comparison             200
Google            Google-CloudVertexBot  /research/foo           200

Then track:

• unique URLs crawled
• crawl frequency
• last crawl date per URL
• HTTP status
• bytes transferred
• crawl depth
• AI operator
• bot type: search vs agent vs training
• robots.txt compliance
• pages never crawled
• pages crawled unusually frequently
• changes in crawling after publishing/updating content

Cloudflare's own API exposes essentially this kind of data, so you don't necessarily need to build the raw collection layer yourself. Cloudflare Docs

4. xSeek

If you specifically want a dedicated AI-crawler monitoring product rather than a broader CDN/SEO platform, xSeek is worth investigating. Its stated focus is tracking AI bots such as GPTBot, ClaudeBot and PerplexityBot and providing crawl analytics and robots.txt monitoring. xSeek

I'd put it behind Cloudflare for most businesses simply because Cloudflare is already sitting in the request path and therefore has excellent visibility into the traffic.

The important distinction

There's a trap here: “AI indexing” isn't one thing.

You really want to monitor at least three separate behaviors:

1. AI search crawling

“I'm collecting your page so I can potentially use it to answer future searches.”

Examples include OAI-SearchBot, Claude-SearchBot, and PerplexityBot. Cloudflare Docs

2. AI assistant fetching

“A user asked a question and I'm fetching this page right now.”

Examples include ChatGPT-User, Claude-User, and Perplexity-User. Cloudflare Docs

3. Model-training crawling

“I'm collecting content for model training/fine-tuning.”

GPTBot, ClaudeBot, etc. can fall into this category. Cloudflare explicitly separates Search, Agent, and Training behaviors rather than treating every AI bot as equivalent. Cloudflare Docs

That distinction makes your analytics dramatically more useful.

What I'd actually deploy

For a normal company website, my stack would be:

Cloudflare AI Crawl Control + Google Search Console + an AI visibility tracker such as Botify.

That gives you three layers:

Crawl → Index/discovery → Actual AI visibility

For example:

pricing.html was crawled by OAI-SearchBot 17 times → Google has impressions for the page → ChatGPT/AI search mentions the company for relevant prompts.

That's much more actionable than a generic “AI traffic” number.

If you tell me what kind of site you have (SaaS, ecommerce, publisher, docs, local business, etc.) and roughly how many pages, I can narrow this down to the 2–3 tools I'd actually buy/use, including current pricing and what each can and can't measure.

Yes. There are several categories of tools, depending on what you mean by "detects."

1. Reverse proxy / CDN detection (best for production)

These sit in front of your site and can identify AI crawlers before requests reach your application.

The most mature option is:

• developers.cloudflare.com - Identifies many known AI crawlers
• Shows which crawlers are requesting which URLs
• Lets you allow, block, or (in some cases) charge specific crawlers
• Can report robots.txt compliance and suspicious crawler behavior beyond simple user-agent matching on higher-tier plans. Cloudflare Docs Cloudflare Docs

This is currently one of the few widely available products built specifically for AI crawler visibility.

2. Application-level logging

If your content is behind authentication or a paywall, many organizations build middleware that logs:

• User-Agent
• IP address
• ASN (cloud provider)
• Request frequency
• Authentication state
• Session cookies
• Referrer
• HTTP status codes

From this you can detect:

• GPTBot
• ClaudeBot
• PerplexityBot
• OAI-SearchBot
• ChatGPT-User
• Google-Extended
• Applebot-Extended
• Common Crawl

and determine whether they are:

• hitting paywalled URLs
• receiving 401/403/402 responses
• bypassing expected flows
• requesting premium content directly

3. WAF / bot-management platforms

Enterprise bot-management products (Cloudflare, Akamai, DataDome, HUMAN Security, etc.) go beyond user-agent detection by examining request fingerprints and behavior to distinguish genuine browsers from automated clients. They can often detect bots that spoof browser identities better than application logs alone. Cloudflare Docs Cloudflare

4. Custom "special handling" detection

If by "require special handling" you mean AI agents need a different response, many publishers implement logic such as:

if crawler == GPTBot:
    return robots policy

if crawler == ChatGPT-User:
    return article summary

if crawler == ClaudeBot:
    require authentication

if crawler == PerplexityBot:
    allow citation endpoint

if anonymous human:
    show paywall

This is becoming increasingly common as publishers differentiate between:

• training crawlers
• search/indexing crawlers
• live retrieval agents
• human readers

5. Analytics platforms

Some web analytics products now classify AI traffic separately, but most focus on reporting rather than enforcement. These are useful for questions like:

• Which AI crawlers visit?
• Which pages do they request?
• How often?
• Are they blocked?

Behind-paywall monitoring

If your concern is specifically AI crawlers accessing paid content, the most useful signals are:

Many publishers also generate reports like:

GPTBot:
  Premium pages requested: 143
  Allowed: 0
  Blocked: 143

ChatGPT-User:
  Premium pages requested: 12
  Allowed via licensed API: 12

Unknown browser automation:
  Premium pages requested: 2,491
  CAPTCHA triggered: 2,470

If you're thinking about building such a system rather than buying one, it's quite feasible: combine web-server logs, bot fingerprinting, session analysis, and a maintained database of known AI crawler identities. That approach gives much finer control over authenticated and paywalled content than relying on robots.txt alone.

Yes. There’s now software specifically aimed at this problem, plus conventional observability tools that can do it with a little configuration.

The closest purpose-built option is cloudflare.com. It identifies individual AI crawlers such as GPTBot, ClaudeBot, PerplexityBot, Bytespider, CCBot, and others, and tracks request volume, bandwidth, status codes, paths, and request trends. You can drill down by crawler/operator and allow or block them individually. Cloudflare Docs Cloudflare Docs

For your specific concern—detecting a crawler spike before it causes your origin/API to throttle—I’d think about the architecture like this:

AI crawler → edge/WAF → crawler identification → per-crawler request counters → spike alert → rate limit/challenge/block → origin

Cloudflare gets fairly close out of the box. Its AI Crawl Control dashboard explicitly reports request-volume changes and high-volume crawler activity, while its WAF/rate-limiting layer can enforce controls before requests reach your application. Cloudflare Docs Cloudflare Docs

There are also two broader approaches worth considering:

• CDN/WAF bot management — Cloudflare, Akamai-style bot management, etc. Best if the objective is preventing the spike from reaching your servers. You can distinguish legitimate crawlers and impose different policies.
• Observability — Datadog/Grafana/Prometheus-style monitoring. Parse User-Agent, bot identity, IP/ASN, endpoint and status code into metrics such as requests_per_minute{crawler="GPTBot"} and alert when velocity suddenly increases. This gives you better custom anomaly detection, but enforcement normally happens elsewhere.

One important distinction is monitoring vs. protection. robots.txt alone won't prevent accidental overload; compliant crawlers may still generate bursts, and noncompliant ones can ignore it. Cloudflare, for example, separately tracks robots.txt violations and supports enforcement rules. Cloudflare Docs

If what you're imagining is something like “Datadog specifically for AI crawler traffic”—a dashboard that automatically identifies every AI bot, learns its normal request rate, alerts on a 5× spike, estimates origin/API rate-limit headroom, and automatically throttles the offending crawler—that's a more specialized product category. Cloudflare covers much of the edge side, but there's room between ordinary bot management and application-level rate-limit observability.

I can also map out the existing products in this space and where the gaps are if you're evaluating this as a product/startup idea.

Yes. If by “AI crawler hit data” you mean requests from GPTBot, ClaudeBot, PerplexityBot, Google-Extended, etc., there are a few viable approaches.

Best fit: Cloudflare AI Crawl Control

If your sites run through Cloudflare, this is probably the cleanest enterprise option. Cloudflare’s AI Crawl Control exposes crawler-level metrics through its GraphQL Analytics API, including crawler/operator, requests, paths, status codes, referrals, and data transfer. developers.cloudflare.comahrefs.comwww.honeyb.ai

That means you can build a pipeline like:

Cloudflare → GraphQL API → ETL/ELT → Snowflake/BigQuery/Redshift → existing BI

and then add AI-crawler dimensions to your existing dashboards rather than adopting another analytics UI.

Cloudflare also supports detailed logs and analytics APIs that can be used for broader data pipelines. developers.cloudflare.com

Other options

• Ahrefs Bot Analytics — good turnkey crawler analytics, with server-side tracking and AI-bot filtering, but I'd investigate its export/API capabilities carefully if the requirement is specifically warehouse-first. ahrefs.com
• Honeyb Agent Analytics — interesting if you already have server/CDN logs. It ingests existing log streams, verifies AI bots, and produces crawler-level intelligence. www.honeyb.ai
• DataEase AI — focuses specifically on AI crawler tracking at the edge rather than browser analytics. www.dataease.ai
• Microsoft Clarity Bot Activity — another server-side option, although it's primarily designed to surface the data in Clarity rather than function as a warehouse ingestion layer. learn.microsoft.com

If you already have a BI + warehouse stack

I would not necessarily buy another dashboard. I'd look for a source that gives you raw/aggregatable events and put a small canonical model in your warehouse:

crawler_hit → crawler → operator → URL → timestamp → status → bytes → content_type → verified_bot

Then your existing BI tool can answer things like:

• AI crawls by GPTBot / ClaudeBot / PerplexityBot
• pages most frequently crawled by AI
• AI crawl volume vs. human traffic
• crawl volume by content category
• crawl errors / blocked requests
• AI crawler bandwidth cost
• crawl trends over time
• AI crawler activity correlated with AI referrals/conversions

One important caveat: a crawler hit does not mean your content was cited or surfaced in an AI answer. Microsoft explicitly distinguishes observed bot activity from grounding, citation, or downstream traffic. learn.microsoft.com

If you tell me your BI tool (Tableau, Power BI, Looker, Sigma, etc.) + warehouse (Snowflake, BigQuery, Databricks, Redshift, etc.) + where your web traffic is hosted/CDN'd, I can narrow this down to the best integration and show you the actual architecture.

If your goal is specifically to audit AI crawlers accessing PII and produce defensible compliance evidence, I’d use a layered setup rather than a single “AI governance” product.

My recommended stack

• cloudflare.com — best first layer for the actual web-crawler problem. It identifies AI crawlers, shows requests by crawler/operator/path, tracks robots.txt violations, and lets you allow or block individual crawlers. Its analytics can also be exported as CSV. Cloudflare Docs Cloudflare Docs
• Your existing DLP/data-classification platform — classify URLs/endpoints or underlying records as PII, PHI, financial data, credentials, etc. The important distinction is that “GPTBot accessed /customers/123” isn't enough; you want evidence that /customers/* contains regulated data.
• onetrust.com — useful as the governance/reporting layer if you need formal ownership, risk assessments, policy violations, evidence collection, and audit outputs. OneTrust specifically describes automated evidence/audit outputs and detection of PII/sensitive attributes. OneTrust

What I would actually log

For every crawler request, capture something like:

Then aggregate it into reports such as:

AI Crawler PII Access Report — August 2026

• 14 AI crawlers observed
• 3 accessed PII-classified resources
• 2 violated robots.txt
• 1 was blocked by policy
• 0 unauthorized successful PII accesses after enforcement
• Evidence retained: request logs + policy configuration + classification snapshot

Cloudflare's current tooling is particularly useful here because it can break activity down by crawler, operator, hostname, path, status code, and content pattern, and its API exposes the analytics programmatically. Cloudflare Docs

One important caveat

Don't treat robots.txt itself as a security control. Cloudflare explicitly notes that robots.txt is voluntary; a crawler can ignore it. If the content actually contains PII, enforce the decision at the WAF/application/authentication layer. Cloudflare Docs

So my short answer is:

Cloudflare AI Crawl Control + DLP/data classification + a GRC/privacy platform such as OneTrust.

If you tell me whether this is for a public website, authenticated SaaS app/API, or internal enterprise systems, I can recommend a more specific architecture and the compliance reports/controls I'd implement for GDPR, CCPA/CPRA, HIPAA, SOC 2, or ISO 27001/27701.

If you want to track how often your brand appears in major AI assistants and capture citation/source details, you’re looking for an AI visibility / AI search monitoring tool (sometimes called GEO/AEO tracking), not a traditional SEO rank tracker. These tools run test prompts against AI assistants, record whether your brand appears, and often extract cited URLs and competitors. Tracemetry Foglift

Good options:

• mentionscout.com — strong fit if you want brand mentions + citation details across ChatGPT, Claude, Gemini, Perplexity, and other AI answer engines. It tracks visibility by prompt, competitor comparisons, sentiment, and the sources AI engines cite. MentionScout
• citations.io — focused specifically on AI citations; it shows which answers mention your brand, which URLs are cited, and which competitors are cited instead. Citations.io
• ahrefs.com — useful for a quick baseline check of AI visibility across ChatGPT, Gemini, Perplexity, Copilot, and Google AI Overviews; it also reports cited domains/pages in its visibility analysis. Ahrefs
• sightivo.com — good if you want recurring monitoring of mentions, cited pages, and competitor recommendations. Sightivo
• synectgeo.io — aimed more at teams doing GEO/AEO programs, with visibility scores, mentions, citations, and assistant-level breakdowns. SynectGEO

When evaluating tools, look for these fields:

• Mention rate: % of tracked prompts where your brand appears
• Share of voice: your appearance rate vs competitors
• Exact prompt tested: what users asked the AI
• Assistant breakdown: ChatGPT vs Claude vs Gemini vs Perplexity
• Citation URL/page: the source the AI relied on
• Citation loss analysis: who gets cited when you do not
• Historical tracking: changes over weeks/months

For a marketing team, I’d usually start with:

1. Citations.io or MentionScout for citation-level monitoring.
2. Ahrefs AI Visibility Checker for a quick benchmark.
3. A more enterprise GEO platform if you need many brands, markets, or thousands of prompts. MentionScout Citations.io Ahrefs

If you tell me your brand size (startup, SaaS, ecommerce, enterprise) and which assistants matter most (ChatGPT only vs all of them), I can narrow this down.

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.

Yes. There’s now a small but growing category of tools specifically aimed at auditing AI-generated citations and making AI outputs defensible for legal/compliance purposes.

A few worth looking at:

• marketplace.microsoft.com — specifically validates legal citations, detects potentially hallucinated case law, checks whether authorities exist and whether they have been overruled/retracted, and creates evidence bundles. It offers enterprise/private-deployment options. Microsoft Marketplace OrchIntel
• kingsfield.ai — positioned as a citation-verification layer for legal AI. It checks citations against court records and produces a signed audit artifact; it currently covers federal law plus a growing set of states. Kingsfield
• legalaispace.com — more of a system-of-record for AI supervision. It can take outputs from ChatGPT, Copilot, Harvey, etc., independently verify citations, apply firm policies, require lawyer sign-off, and retain evidence. Legal AI Space
• sorena.io — broader compliance/research platform that emphasizes source-backed regulatory answers, cross-checking, and audit-ready evidence. Sorena AI
• github.com — an open-source project that goes beyond citation existence: it attempts to determine whether the cited authority actually supports the proposition the AI made. GitHub

The important distinction

If you're thinking about enterprise-wide auditing of assistants, rather than just checking citations in a legal brief, I'd divide the market into three layers:

1. Citation validation — Does the cited source exist? Is the citation accurate? Is the authority still good law?
2. Entailment/grounding validation — Does the source actually support the claim the assistant made?
3. Governance/audit — Who asked the question, which model produced the answer, what sources were retrieved, what policies applied, what risk was detected, and who approved the output?

The third is where things get more interesting for enterprise compliance. A citation by itself isn't really an audit trail; you ideally want the answer → claim → citation → source passage → retrieval event → policy/risk decision → reviewer chain preserved. Progress.com

And this isn't merely theoretical: a California attorney was sanctioned in August 2026 after submitting AI-generated fictitious legal citations, reinforcing why organizations are looking for independent verification rather than simply trusting an assistant's citations. reuters.com

If you're asking because you're evaluating or building such a tool, I can also map out the current vendors and what an enterprise-grade “citation risk auditor” would need to detect (hallucinated citations, outdated law, citation/claim mismatch, missing authority, unauthorized sources, privilege leakage, etc.).

If your specific goal is “create content outlines that increase the odds of being cited/quoted by ChatGPT, Perplexity, Gemini, Google AI Overviews, etc.”, I’d separate the problem into outline generation and AI-search visibility measurement. No single traditional SEO outline tool completely solves both. xSeek Cakewalk

My picks

• Best overall: Frase — probably the best starting point if your immediate need is research → questions → headings → writer-ready brief. Its strength is brief/outline generation, and it’s considerably cheaper than premium enterprise tools. Honest AI Guide GTM Help
• Best for sophisticated content strategy: MarketMuse — better if you're building a large topical ecosystem rather than producing isolated articles. It can identify topical gaps and help structure content around broader authority. Honest AI Guide
• Best for optimization after the outline: Surfer — excellent for SERP-driven structure and semantic coverage, particularly if you want the writer to optimize the finished article against competing pages. Honest AI Guide
• Best premium editorial option: Clearscope — useful when you have professional writers/editors and care heavily about semantic coverage and editorial quality. GTM Help
• Best specifically for AI-search visibility: add a GEO/AEO visibility platform — tools such as Profound, Scrunch, AthenaHQ, or Semrush's AI Visibility capabilities are aimed at measuring whether AI systems actually mention/cite you, rather than merely assuming traditional SEO optimization will translate into AI citations. xSeek The AI Rankings

The important distinction

I wouldn't optimize an outline simply for “AI quotation.” I'd design it around characteristics that make individual claims easy for an answer engine to retrieve and cite:

1. Answer the primary question immediately.
2. Give each important sub-question its own section.
3. Put one clear claim/answer near the beginning of each section.
4. Support important claims with specific evidence, statistics, dates, methodology, or primary sources.
5. Use descriptive H2/H3s that resemble the questions people actually ask.
6. Include original data, expert observations, comparisons, definitions, and concrete examples—not just generic summaries.
7. Make important passages self-contained so an AI can quote them without needing three paragraphs of surrounding context.
8. Cover related questions comprehensively, but avoid padding.
9. Keep facts and claims attributable to identifiable sources.

That's because traditional SEO tools mostly reverse-engineer ranking pages, whereas AI-answer systems have somewhat different citation behavior. Cakewalk

What I'd actually use

For a small/medium content operation, I'd use:

Frase → ChatGPT/Claude → human editing → AI-visibility tracker

Frase creates the research-driven outline; the LLM turns that into a draft; the editor adds genuine expertise/evidence; then the visibility tool tells you whether the resulting content is actually appearing in AI answers.

If you tell me what type of content you're producing (B2B SaaS, affiliate, local SEO, thought leadership, ecommerce, etc.) and roughly how many articles/month, I can recommend a specific stack and workflow—including which tool I'd pay for and which ones I'd skip.

Yes. If your budget is limited, I wouldn't start with an expensive “GEO/AEO” platform. For ecommerce, the bigger opportunity is making your product data easy for AI systems to understand, quote, compare, and recommend—not simply stuffing descriptions with keywords.

For a Shopify store, I'd look at these:

What I'd actually do with a small budget

I'd spend $0–$30/month initially, rather than buying a fancy GEO platform.

The product description itself should be structured more like product knowledge than traditional marketing copy:

What it is → who it's for → important attributes → use cases → differentiators → specifications → compatibility → common questions

For example, instead of:

“Our premium everyday backpack combines timeless style with exceptional functionality. Perfect for adventures or your daily commute.”

Use something closer to:

20L waterproof commuter backpack designed for laptops up to 16 inches. It has a padded laptop compartment, two external water-bottle pockets, and a waterproof recycled-nylon exterior. Best suited for commuting, business travel and everyday carry. Weight: 1.8 lb. Dimensions: 18 × 12 × 6 in.

That gives an AI answer engine specific, verifiable facts to work with.

The overlooked part: FAQs

I'd also add questions that resemble the questions shoppers actually ask AI:

• Is this backpack waterproof?
• What size laptop fits?
• How much does it weigh?
• Is it suitable for airline travel?
• What material is it made from?
• Does it have a water-bottle pocket?
• Who is this product best for?
• How does it compare with a 20L commuter backpack?

A Shopify app such as StoreFAQ specifically supports AI-generated product FAQs and schema, and currently has a free plan. Shopify App Store

One important warning

Don't assume that an app claiming “GEO optimized” = your products will appear in ChatGPT/Google AI answers. There isn't a magic description format that guarantees that.

Your goal is to make the underlying product information:

accurate + specific + comprehensive + consistent + machine-readable + independently corroborated.

Recent ecommerce guidance around AI shopping is increasingly emphasizing structured, granular product data rather than merely generating prettier copy. Express Computer

If you tell me what platform you're on (Shopify, WooCommerce, BigCommerce, etc.) and roughly how many products you have, I can narrow this to the 2–3 cheapest tools I'd actually use and give you a practical AI-search optimization workflow.

20L waterproof commuter backpack designed for laptops up to 16 inches. It has a padded laptop compartment, two external water-bottle pockets, and a waterproof recycled-nylon exterior. Best suited for commuting, business travel and everyday carry. Weight: 1.8 lb. Dimensions: 18 × 12 × 6 in.

That gives an AI answer engine specific, verifiable facts to work with.

The overlooked part: FAQs

I'd also add questions that resemble the questions shoppers actually ask AI:

• Is this backpack waterproof?
• What size laptop fits?
• How much does it weigh?
• Is it suitable for airline travel?
• What material is it made from?
• Does it have a water-bottle pocket?
• Who is this product best for?
• How does it compare with a 20L commuter backpack?

A Shopify app such as StoreFAQ specifically supports AI-generated product FAQs and schema, and currently has a free plan.

One important warning

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.

For a healthcare website where accuracy and source control are critical, the key capability to look for is a retrieval-augmented generation (RAG) / grounded knowledge retrieval tool. These tools help AI assistants answer from your approved, vetted content instead of relying only on the model’s general training data. ClinRAG

Useful options and approaches include:

• A RAG-based knowledge assistant — Index your approved website pages, clinical FAQs, policies, patient education materials, and guidelines. The assistant retrieves relevant passages first, then generates an answer from those sources. This is the core pattern used for source-grounded healthcare assistants. Lumen PubMed Central (PMC)
• Citation/provenance features — Require the assistant to show where answers came from (for example, linking back to the exact approved page or document section). This makes review and auditing easier. Lumen medground
• Content governance controls — Use tools that let your team control the source corpus, remove outdated materials, enforce permissions, and restrict answers when no approved source is found. Some enterprise platforms include “ground only” behavior to prevent unsupported responses. Chitika
• Evaluation and monitoring tools — For healthcare, it is useful to continuously test answers against a vetted reference set, checking for hallucinations, missing citations, and unsafe responses. Runvecta

A typical architecture would be:

1. Maintain a single approved content repository (your reviewed webpages/documents).
2. Index it with a RAG retrieval layer.
3. Connect the AI assistant so it answers only from retrieved content.
4. Require citations and log interactions for audit.
5. Regularly evaluate responses with clinical/content reviewers.

If you mean AI assistants like ChatGPT, Google, or other public assistants discovering your website content, the solution is slightly different: you would focus on structured content, clear authoritative pages, machine-readable metadata, and official content feeds/APIs where available. If you mean a chatbot embedded on your healthcare website, a RAG knowledge assistant is usually the relevant tool category.

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

Yes. There’s now a fairly clear category emerging around AI visibility / AI share of voice (SOV) for ecommerce brands. The important distinction is that some platforms measure mentions/citations, while others are starting to measure actual shopping recommendations.

Platforms worth looking at

• stackline.com — probably one of the more interesting options if you're a serious ecommerce/consumer brand. It says it tracks millions of real shopper questions and measures product recommendation/impression share across conversational and agentic platforms, including ChatGPT and Amazon's shopping assistant. Stackline
• crowdreply.io — explicitly calculates SOV by brand, LLM, prompt category and intent, with daily prompt runs across multiple models. CrowdReply
• seerly.app — tracks ChatGPT, Perplexity, Claude and other engines, with per-engine visibility, competitor SOV and trends over time. Seerly
• brandofy.ai — more AEO-oriented; tracks brand/product mentions across ChatGPT, Gemini, Google AI Overviews/AI Mode and Perplexity, including SOV and missing opportunities. It specifically positions itself for DTC/ecommerce. Brandofy
• verityscore.io — measures AI SOV across ChatGPT, Perplexity, Gemini, Claude and Mistral, with an ecommerce/Shopify orientation. Verity Score
• actowizmetrics.com — particularly interesting for competitive benchmarking: it reports recommendations across ChatGPT, Gemini, Perplexity and Amazon Rufus, by category. Actowiz Metrics

The key thing I'd look for

If you're evaluating this for an ecommerce site, I wouldn't settle for a generic "AI visibility score." I'd want a dashboard roughly like:

That's because being mentioned isn't necessarily the same as being recommended, and being recommended isn't necessarily the same as driving traffic or sales.

There's also an important second category emerging: agentic commerce measurement. Tru Commerce, for example, is measuring SOV across ChatGPT, Gemini, Perplexity, Rufus, Copilot and Claude and tying that to agentic-commerce signals. Tru Commerce

So if what you're really asking is "Is there a Semrush/Ahrefs-like platform that tells an ecommerce brand its share of product recommendations across ChatGPT + Gemini + Perplexity + Amazon + other shopping agents?" — yes, but the market is still immature. Stackline is probably one of the closest fits to that broader ecommerce use case; CrowdReply/Seerly/Brandofy are closer to the AI-SOV/AEO monitoring side.

If you're considering building or buying one, I can also map out the current landscape by platform × assistants covered × ecommerce/product-level tracking × SOV methodology × pricing, which makes the gaps in the market pretty interesting.

If you’re evaluating this specifically for an enterprise AI-search / assistant visibility program, my shortlist would be Profound and Peec AI, with Profound as my default enterprise pick and Peec as the strongest alternative if share-of-voice analytics and ease of use are the priority.

1. Profound — best overall for an enterprise

Profound is particularly strong if you need something that can become a central measurement system for SEO/AEO, brand, content, and executive reporting.

Its competitive benchmarking measures visibility score, visibility rank, citation share, share of voice, sentiment, and average position. Importantly, it can identify competitors based on who is actually being cited in AI answers—not just competitors you manually define. Profound

It also tracks across a broad set of AI surfaces, including ChatGPT, Perplexity, Google AI Overviews, Gemini, Microsoft Copilot, Grok, and DeepSeek. Profound

For enterprise reporting, its customizable AEO dashboards support metrics such as Visibility Score, Share of Voice, Average Position, and Citation Rank, with shareable dashboards and PDF exports. Profound

I'd choose Profound if: you have multiple brands/products, a substantial SEO/content organization, need executive reporting, or want to connect AI visibility to a broader enterprise AEO strategy.

2. Peec AI — best if competitive SOV is the core job

Peec has a particularly clean model around:

• Visibility — percentage of AI responses mentioning you
• Position — where you appear
• Sentiment
• Share of Voice versus competitors
• Prompt-level competitive comparisons
• Citation/source intelligence

It refreshes tracked prompts daily and lets you segment by AI model, country, and prompt tags, which is useful if you're measuring different markets, personas, or funnel stages. Peec AI Peec AI

It also supports API, Looker Studio, and MCP integrations, making it attractive if you want to pipe AI visibility data into an existing BI environment. Peec AI

Peec has also recently expanded into brand perception, measuring the attributes AI associates with a brand and how those attributes compare with competitors. Peec AI

I'd choose Peec if: your primary question is “How much of the AI conversation do we own versus competitors, and why?” and you want a relatively straightforward analytics experience.

What I'd measure regardless of platform

Don't reduce this to a single "AI visibility score." For an enterprise, I'd build the scorecard around:

1. AI Share of Voice — percentage of category answers mentioning your brand.
2. Visibility by model — ChatGPT vs. Gemini vs. Perplexity vs. Google AI surfaces.
3. Competitive rank — where you appear when multiple brands are recommended.
4. Citation share — how much of the underlying source/citation ecosystem you own.
5. Recommendation rate — percentage of high-intent prompts where the assistant recommends you.
6. Sentiment / positioning — what the assistant says about you.
7. Prompt-level win rate — which commercially important questions you win/lose.
8. Source influence — which publishers, review sites, Reddit threads, etc. are driving competitors' visibility.
9. Geographic visibility — especially important for multinational enterprises.
10. Trend — SOV and competitive position over time.

The distinction between brand mention share and citation/source share is particularly important. A company can be frequently mentioned while having little control over the sources influencing the answer—or have highly authoritative content cited without the brand itself being mentioned. Peec explicitly tracks both concepts. Peec AI

My recommendation

For a serious enterprise deployment, I'd demo Profound first and Peec second.

If your buying committee asks, “Which one gives us the best measurement of competitive share of voice in AI assistants?”, I'd give Profound a slight edge because of its combination of competitor discovery, SOV/citation metrics, breadth of AI platforms, and enterprise reporting. Profound Profound

If the primary stakeholder is a growth/SEO team that wants fast, actionable competitive intelligence, I'd take a very close look at Peec. Its visibility/SOV model is exceptionally aligned with that use case. Peec AI

If you tell me your enterprise size, industry, number of brands/products, and which AI assistants matter most (ChatGPT/Gemini/Perplexity/Copilot/etc.), I can give you a Profound vs. Peec vs. 3–5 other platforms comparison, including likely pricing, data coverage, APIs, enterprise controls, and what I'd buy.

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.

Yes. For a small ecommerce store, there are now several free AI-visibility checkers that can tell you whether your products/site are being mentioned or cited in AI answers.

Best options

• Ecomrank AI Visibility Checker — probably the best fit for you. Enter your store URL + what you sell. It tests buyer-style questions against ChatGPT, Perplexity, and Google AI Overviews and shows whether you're cited, who gets recommended instead, and which questions you're missing from. Free/no signup.
• AI Peekaboo Product Visibility Checker — particularly useful if you want to test individual product pages. Paste a product URL and it checks things like price, benefits, product schema, "best for" positioning, and other signals that affect AI recommendations.
• Wildcard's free ecommerce AI audit — broader ecommerce audit covering ChatGPT, Gemini, Perplexity, Claude, and Google AI Overviews, including which products appear in tested answers.
• Ahrefs Free AI Visibility Checker — good for checking your brand/product name across ChatGPT, Gemini, Perplexity, Copilot, and Google AI Overviews. No signup for the free check.

One important distinction

There are really two different things you want to measure:

1. "Can AI read my product page?"

→ Check crawlability, schema, product data, etc. Tools like AI Peekaboo are useful for this.

2. "When someone asks AI for the type of product I sell, does it actually recommend/cite me?"

→ Ecomrank/Wildcard are more useful. This is the metric I'd care about most as an ecommerce owner.

For example, if you sell handmade leather wallets, don't just test "Is my product page visible?" Test questions such as:

"What's the best handmade leather wallet under $100?"
"Best minimalist leather wallet for men?"
"Where can I buy a vegetable-tanned leather wallet?"

Then see which products and domains ChatGPT/Perplexity actually recommend.

If you give me your store URL and what you sell, I can also help you come up with the 10–15 buyer questions I'd test to see whether you're showing up in AI answers.

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're a non-technical founder and mean “paste my website/brand in and tell me whether AI engines can see/recommend me,” I’d start with rank.ai.

My picks

1. Best overall: Rank.ai

• Free
• No registration
• No API key or setup
• Tests ChatGPT, Claude, and Gemini
• You give it a prompt + your domain and it shows whether you're mentioned/cited. Rank.ai
• Best for a deeper website audit: firecrawl.dev

• Free, no signup, no API key
• Checks crawler access, structured data, content citability, entity clarity, llms.txt, etc.
• Better for answering “what should I fix on my website?” than “does ChatGPT recommend me?” Firecrawl
• Best for actual brand visibility across more engines: citedspy.com

• No signup
• Checks ChatGPT, Perplexity, Gemini, Claude and Grok
• Gives visibility score, competitors, rankings, sentiment and cited sources. CitedSpy
• Simplest technical/accessibility check: isvisible.ai

• No signup
• Paste your domain
• Checks whether AI crawlers can access your site and gives a 0–100 score. isvisible.ai

What I'd actually do

For a founder, I'd run Rank.ai first, then Firecrawl.

That's because “AI visibility” has two different meanings:

A. “Does AI recommend my company?” → Rank.ai / CitedSpy
B. “Is my website technically accessible and understandable to AI?” → Firecrawl / IsVisible

Don't put too much weight on a single “AI visibility score.” The most useful signal is whether you appear for the specific questions your potential customers actually ask, and which competitors and sources appear instead.

If you tell me what your company does + your website, I can also tell you exactly which 5–10 prompts I'd use to test your AI visibility.

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.

If you mean one agency reporting platform that combines AI-assistant visibility with traditional SEO metrics, the strongest match is agencyanalytics.com.

It puts AI visibility from ChatGPT, Google AI Overviews/AI Mode, Claude, Perplexity, and Gemini alongside conventional metrics such as Google Search Console, Google Analytics, rankings, traffic, PPC, and social data, and feeds them into the same automated client reports. It also supports white-label reporting. AgencyAnalytics

Another particularly relevant option is rankability.com. It explicitly combines traditional Google rankings + AI mentions/citations into a single Search Performance Index, which can make the story easier to present to clients. Rankability

My pick for an agency: AgencyAnalytics if the priority is client reporting and integrating lots of existing marketing data; Rankability if the priority is unifying Google and AI-search visibility into one search-specific score.

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.

If you mean an agency tool that tracks how often clients appear in AI-generated answers (ChatGPT, Claude, Gemini, Perplexity, etc.) and creates client-facing reports, you’re looking for an AI visibility / AI search optimization (AIO, GEO) monitoring platform.

Some tools in this category include:

• Profound — Tracks brand visibility across AI answer engines, monitors mentions, citations, sentiment, and competitive share of voice. Designed for enterprise teams and agencies managing client reporting.
• OtterlyAI — Monitors how brands appear in AI search results and provides visibility reporting.
• Peec AI — Tracks mentions and rankings in AI assistants and provides reporting around brand presence.
• Scrunch AI — Helps companies monitor and improve how they are represented in AI-generated answers.
• Writesonic — Includes AI search visibility features alongside broader content tools.

For an agency workflow, the ideal features to look for are:

• Client dashboards with white-label reports
• Tracking across multiple assistants (ChatGPT, Claude, Gemini, Perplexity, Copilot)
• Prompt monitoring (“When users ask X, is our client mentioned?”)
• Competitor comparisons
• Citation/source tracking (which websites AI assistants reference)
• Sentiment and positioning analysis
• Scheduled PDF/email reports for clients

If you’re an agency managing many brands, platforms like Profound or OtterlyAI are closer to a client-reporting model, while lighter tools may be better for internal SEO teams.

If your agency’s core need is “show the client that AI visibility changed after we updated content, then turn that into a polished report,” I’d shortlist these:

1. tryprofound.com — best overall for an agency

This is probably the closest match to your workflow. Profound lets you:

• Track Visibility Score, Share of Voice, Average Position, and Citation Rank over time.
• Run prompts daily, so you can establish a before/after baseline around a content update. Profound Profound
• See which pages AI engines cite and how citation/visibility changes.
• Connect the visibility data to content optimization workflows, including refreshing existing pages. Profound
• Maintain separate client workspaces through its agency offering. Profound
• Create client dashboards with PDF exports and public links, so you don't have to rebuild a report every month. Profound

The particularly useful feature for your use case is that Profound explicitly supports tracking performance before and after campaigns. Profound Knowledge Base

2. otterly.ai — best if reporting simplicity matters

OtterlyAI is worth considering if you want something more focused on monitoring/reporting rather than a large AEO platform.

It tracks mentions, rankings, share of voice and the actual URLs cited by AI engines, across ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini, Copilot and Claude. Otterly

For agencies, it has:

• Multiple client workspaces
• Scheduled, branded reports
• PDF/CSV exports
• Looker Studio integration
• Content audits and recommendations
• API access if you want to build your own reporting layer. Otterly

That makes it attractive if your desired workflow is basically baseline → content change → monitor → automatically report delta.

My pick

I'd start with Profound if you're selling an ongoing AEO/GEO service and want the platform to help you both prove results and decide what content to change.

I'd choose OtterlyAI if you already have your content/SEO workflow figured out and mainly need a multi-client AI visibility tracker + client reporting engine.

One important thing I'd test in a demo: whether the platform lets you annotate a specific content publication/update date and cleanly isolate the visibility change afterward. That's much more useful for client ROI reporting than simply showing a generic 30-day visibility trend.

A good client report would ultimately look like:

Content update: Product comparison page rewritten June 3
AI visibility: 18% → 37%
Share of voice: 2.1% → 5.8%
Citations: 4 → 13
AI engines improved: ChatGPT, Perplexity, Gemini
Newly cited URL: /comparison-page
Business takeaway: Client is now appearing in X% more relevant AI answers

That before/after + “what changed” + evidence of citations is the reporting structure I'd optimize the tool choice around.

If your priority is seamless client reporting, I’d recommend otterly.ai.

Why it stands out:

• API access: You can programmatically pull AI visibility metrics such as brand mentions, citations, share of voice, sentiment, and prompt-level results into your own systems. Otterly Otterly
• BI/reporting integrations: Its Looker Studio connector lets you blend AI visibility with GA4, Search Console, and other marketing data in a single client dashboard. It also supports feeding data into Tableau, Power BI, and other BI tools through the API. Otterly Otterly
• Agency-friendly: Separate workspaces can isolate each client's brands, prompts, reports, and access. Otterly
• Automated reporting: Looker Studio dashboards can be branded and scheduled for recurring client delivery. Otterly Otterly
• Raw exports: CSV and JSON exports are available if you need to push data through your own ETL/CRM workflow. Otterly

If CRM integration is the key requirement

I'd shortlist OtterlyAI and tryprofound.com.

Profound is particularly interesting if you have a data/engineering team: its API can push visibility score, share of voice, and citation rank into Looker, Tableau, Mode, or Hex, and it provides recipes for scheduled reporting and custom workflows. Profound

My pick: OtterlyAI for an agency that wants to get client reporting running quickly; Profound if you're building a more customized data pipeline.

If you tell me which CRM/reporting suite you use (e.g. Salesforce, HubSpot, Looker Studio, Power BI, Tableau, AgencyAnalytics, Databox), I can narrow this down to the platform with the cleanest integration.

If your goal is specifically monitoring how AI assistants talk about your brand—including whether the sentiment is positive/negative, how often you're mentioned, what competitors are recommended, and which sources are cited—you want an AI visibility / AEO monitoring platform, not traditional social-listening software.

My shortlist

My pick: Profound

For a serious brand-monitoring program, I'd start with Profound. Its Answer Engine Insights product explicitly measures sentiment, visibility, citations, share of voice and positioning, while querying the actual answer engines on a recurring basis. Profound Knowledge Base

That distinction matters: you don't merely want to know "Did ChatGPT mention us?" You want something closer to:

What does ChatGPT say about us? Is that portrayal favorable? How does it compare with competitors? Which prompts produce negative perceptions? Which sources are causing that perception? Is the answer changing over time?

Profound currently covers a particularly broad set of AI surfaces, including ChatGPT, Claude, Gemini, Perplexity, Copilot, Grok, DeepSeek and Google's AI experiences. Profound Profound

One important caveat

"Sentiment" in AI answers isn't quite the same as social-media sentiment. A brand can be mentioned positively but still lose the recommendation, for example:

"Brand X is reliable, but Brand Y offers better value."

So I'd prioritize platforms that let you analyze sentiment + recommendation/positioning + share of voice + citations, rather than buying a tool based solely on a positive/neutral/negative score.

If you tell me your company size and approximate monthly budget (e.g. <$100, $100–500, $500–2k, enterprise), I can narrow this to the top 2–3 options and compare pricing, assistant coverage, sentiment accuracy, alerts, API access, and reporting.

The category you’re looking for is usually called AI brand monitoring, LLM visibility tracking, or GEO/AEO monitoring (Generative Engine Optimization / Answer Engine Optimization). These tools generally don’t “crawl” the private internals of ChatGPT, Claude, or Gemini; instead, they run controlled prompts against AI assistants (often via APIs or automated sessions), capture the answers, extract company mentions, and score things like sentiment, rank/position, citations, and competitor mentions. MentionsAPI Foglift

Some options:

If you want to build your own pipeline, the typical architecture is:

1. Prompt library

• “best tools for [category]”
• “[company] alternatives”
• “is [company] trustworthy?”
• “[company] reviews”
• AI assistant runners

• APIs where available (OpenAI, Anthropic, Google, etc.)
• Browser automation only where permitted by the service terms
• Extraction layer

• Detect company mentions
• Pull surrounding snippets (e.g., ±1–3 sentences)
• Identify competitors mentioned nearby
• Sentiment classifier

• Positive / neutral / negative
• Topic tags (“pricing”, “support”, “quality”, “security”)
• Confidence score
• Dashboard

• Mention frequency
• Sentiment trend
• Share of voice
• Which prompts produce negative descriptions

For a marketing team, I’d start with a dedicated tool rather than scraping assistants directly, because AI responses vary by prompt, user context, model version, and whether the assistant has web access. Reddit

If you mean “crawl all publicly visible AI answers on the web” rather than “query AI assistants and analyze their outputs,” that is a different problem and usually involves web monitoring plus LLM extraction.

If you mean “we changed specific pages on our website, and we want to know whether those changes caused AI answers about us to become more positive/negative”, the strongest fit I found is tryprofound.com.

It gets fairly close to that exact workflow:

• Tracks AI answers daily across ChatGPT, Perplexity, Gemini, Google AI Overviews, Claude, etc., for defined prompts. Profound Knowledge Base Profound
• Measures sentiment over time, including positive/negative themes and the specific claims AI is making about your brand. Profound Profound Knowledge Base
• Connects sentiment to citations, so you can see which pages/sources are influencing the narrative. Its newer sentiment system drills down to the citations behind individual claims. Profound
• Tracks watched URLs/pages, which is useful for isolating the pages you changed. Profound Knowledge Base
• Its Agent tooling can compare sentiment across time periods, including identifying changes after product/content updates. Profound Knowledge Base

One important distinction

If you specifically need causal attribution—e.g.:

“We rewrote /pricing on August 3 → AI sentiment for pricing prompts improved 18% over the next 14 days → these 7 answers began citing the revised page.”

I'd treat Profound as the measurement layer, but you'd want to maintain a content-change log alongside it (Git/CMS history, deployment timestamps, etc.). The available documentation shows strong correlation/drill-down capabilities, but I wouldn't assume it can prove causality from a website edit alone.

There is also jujugeo.com, which explicitly advertises a “What moved” feed and says it can identify when a citation follows a fix you applied. That makes it particularly interesting if your priority is connecting changes to subsequent AI-search movements. JujuGEO

My shortlist: Profound for deeper sentiment/claim/citation analysis; jujuGEO if the primary requirement is a straightforward “what changed after our fix?” workflow.

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.