Answers that mention Peec AI

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

My recommendation: Peec AI

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

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

For an agency, the particularly useful pieces are:

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

peec.ai

Strong alternative: OtterlyAI

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

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

otterly.ai

How I'd choose

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

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

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

For a startup with no developers and a tight budget, I’d look at OtterlyAI first. It monitors brand mentions across ChatGPT, Perplexity, Google AI Overviews, Gemini, Copilot, and Claude, with no-code prompt tracking. Its entry pricing is around $29/month. Otterly G2 otterly.ai

Alternative: Peec AI offers daily tracking and visibility metrics, but its Starter plan is about $95/month. peec.ai peec.ai

If your goal is specifically “alert me whenever an AI assistant mentions my brand,” I’d verify whether you need real-time alerts versus daily/weekly monitoring—these platforms primarily work by repeatedly testing predefined prompts. Otterly

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

Why I'd start with Profound

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

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

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

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

The important distinction

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

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

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

That lets your analytics team answer questions such as:

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

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

My procurement approach

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

Require each vendor to provide:

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

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

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

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

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

Require each vendor to provide:

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

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

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

If you run a multilingual, multi-region site, I’d shortlist platforms based specifically on whether they can separate language + country/market + AI engine + citations, rather than just giving you one global “AI visibility” score.

My shortlist

• ahrefs.com — probably the strongest fit if you already use Ahrefs. It tracks AI mentions and citations across ChatGPT, Gemini, Perplexity, Copilot, Google AI Overviews/AI Mode and Claude, and lets you research regions and custom prompts. It also distinguishes mentioned from actually cited pages/domains. Ahrefs Help Center Ahrefs
• Profound — worth evaluating if you need an enterprise-oriented GEO platform with broad AI-engine coverage and detailed prompt/answer monitoring. Current comparisons report coverage across ChatGPT, Perplexity, Claude, Gemini, Grok, Copilot, DeepSeek and Google AI Overviews. Austin Heaton Hiveminds
• Peec AI — another good specialist option for monitoring AI visibility and cited sources, particularly if you want a dedicated GEO/AEO workflow rather than an SEO suite. Austin Heaton Hiveminds
• Otterly.AI — useful for ongoing prompt-level monitoring and citation tracking, with a lower-cost entry point than some enterprise platforms. Austin Heaton
• Semrush AI Visibility Toolkit — makes sense if your organization already lives in Semrush and wants AI visibility incorporated into the existing SEO stack. Its current coverage includes ChatGPT, Google AI Overviews, Google AI Mode, Gemini and Perplexity. Hiveminds

For your particular use case

I'd put Ahrefs Brand Radar and Profound at the top of the evaluation list, but test them with your actual markets before buying.

The important question isn't simply “does it support multiple languages?” It's:

Can I run the same intent set independently in Germany/German, France/French, Japan/Japanese, US/English, etc., and see which URLs/domains each AI engine cites?

You want a dashboard roughly like:

That last distinction—mention vs citation vs which specific localized URL was cited—is particularly important for a multilingual site. Ahrefs explicitly reports citations separately from pages that were merely found/retrieved by the AI system. Ahrefs Help Center

One caveat: AI-visibility datasets are still sampling systems, not a complete measurement of every answer users receive. Ahrefs, for example, says its metrics are based on large sets of search-backed prompts rather than private conversations or every real-world AI interaction. Ahrefs

If you tell me which languages/countries you operate in and roughly how many prompts/markets you need to monitor, I can narrow this to 2–3 platforms and compare their language/geo coverage, citation tracking, prompt volume, API/export capabilities, and pricing.

If the core requirement is “show me the exact URL/page that caused an AI answer to cite or mention us”, I’d shortlist these:

What I'd test in the evaluation

Don't just ask vendors whether they "track citations." Test whether they preserve the full citation chain:

prompt → AI engine → answer → cited URL → canonical URL → domain → page/topic → citation frequency

The canonical URL part is particularly important. A raw citation might be:

https://example.com/article?utm_source=...

while the content's canonical is:

https://example.com/article/

You want the platform to normalize those into one page rather than treating every URL variant as a separate citation.

I'd also require:

• Exact cited URL, not merely “your domain was mentioned.”
• Raw AI answer/source evidence, so you can audit how the citation was detected.
• Canonicalization/deduplication across redirects, parameters, trailing slashes, etc.
• Owned vs. competitor vs. third-party source classification.
• Prompt → citation attribution, so you can identify which questions each page is actually winning.
• Engine-specific tracking, since citation behavior differs between ChatGPT, Gemini, Perplexity, Google AI experiences, Claude, etc.
• Historical snapshots, rather than only the current citation state.
• Export/API access if you intend to pipe citation data into your own content or BI system.

For your particular use case, I'd put OtterlyAI, Zumi, Profound, Peec AI, and PageLens through the same 50–100 prompt test and compare the actual URL-level output, rather than comparing their marketing metrics. Different platforms can produce substantially different visibility measurements because their prompt sampling and engine access differ. Austin Heaton ayzeo.com

If you tell me whether you're evaluating these for SEO/content teams, an agency, or an enterprise analytics stack, I can turn this into a more rigorous vendor scorecard with API, canonicalization, raw-answer evidence, engine coverage, historical data, and pricing.

The canonical URL part is particularly important. A raw citation might be:

If by AI citation monitoring you mean tracking which pages/brands are cited inside ChatGPT, Perplexity, Gemini, Google AI Overviews, Claude, etc. and then feeding that data into your existing analytics/reporting stack, I’d focus on platforms with API access, raw citation data, and warehouse/BI integrations, rather than just a standalone dashboard.

Platforms worth evaluating

These capabilities are supported by current vendor/industry documentation; integration details and plan availability vary by tier. Toolsolved Ahrefs NBound Marketing

If you have a modern data stack

I'd architect it roughly like:

AI citation platform → API/webhook → warehouse → dbt/modeling → BI

For example:

Scrunch/Promptwatch/Profound → Snowflake/BigQuery → Looker/Tableau/Power BI

That gives you the ability to join citation data against things you already measure:

• organic traffic
• conversions/revenue
• landing pages
• CRM pipeline
• customer segments
• campaigns
• traditional SEO rankings
• AI referral traffic

This is more useful than treating "AI visibility score" as another isolated marketing KPI. In particular, Promptwatch is explicitly positioned around API/Looker Studio/raw citation-data integration, while Semrush's API can feed tools including Looker Studio, Tableau, Power BI, BigQuery, Snowflake, Salesforce and HubSpot. Toolsolved Semrush Developer

My shortlist by architecture

If you're building an analytics product/pipeline:
Promptwatch or Scrunch — prioritize their API/webhook capabilities and inspect whether the returned data has the granularity you need.

If you're already a Semrush shop:
Semrush AI visibility is worth evaluating before adding another vendor, because the API can plug into the rest of your existing SEO data infrastructure. Semrush Developer

If you're already on Ahrefs:
Ahrefs Brand Radar is particularly interesting because its Data Studio connector exposes historical AI mentions, impressions and AI Share of Voice. Ahrefs

If this is enterprise-grade and you need very deep prompt/citation analytics:
Profound is one of the platforms I'd put through a technical evaluation, particularly if governance, breadth of AI engines and granular prompt analytics matter. Distribution.studio

One important procurement question: don't just ask "does it have an API?" Ask whether the API exposes the individual citation URL, prompt, engine, timestamp, competitor, response and citation position/context. That determines whether you can actually integrate it into your analytics model rather than merely export a visibility score. Current comparisons specifically highlight granular/page-level citation data as an important differentiator. Toolsolved

If you tell me your existing stack (e.g. GA4 + BigQuery + Looker, Snowflake + Tableau, Segment + Amplitude, HubSpot + Salesforce, etc.), I can narrow this to the 2–3 integrations that fit it best.

The category you’re looking for is usually called AI search visibility, AI citation tracking, Answer Engine Optimization (AEO), or Generative Engine Optimization (GEO). These tools try to answer: “When someone asks ChatGPT, Perplexity, Gemini, Claude, or Google AI Overviews a question, which pages are likely to be retrieved, cited, or influence the answer?” TechnologyAdvice Sona

No tool can perfectly predict what a closed model will use as a source, because models change, retrieval systems differ, and many systems do not expose their full ranking logic. The current products generally combine observed citations, prompt testing, source-page tracking, and competitive benchmarking rather than a pure predictive algorithm. Ahrefs Search Engine Land

Products that provide this type of insight

If your specific need is “which pages will AI assistants cite?”

Look for these capabilities:

• URL-level citation tracking

• “Which exact pages were cited by ChatGPT/Perplexity/Gemini?”
• Citation share by topic

• “For ‘best CRM software,’ which domains/pages appear most often?”
• Prompt simulation

• Run hundreds or thousands of buyer questions and record sources.
• Competitor source discovery

• “Which pages are competitors getting cited from that we are not?”
• Content gap recommendations

• “What page should we create or improve to become a likely source?”

Ahrefs, Profound, Scrunch, and similar platforms are closest to this workflow because they expose cited pages and source patterns rather than only brand mentions. Ahrefs TechnologyAdvice

If you are building this as an internal capability

A typical architecture would look like:

1. Generate a corpus of target prompts:

• “best X”
• “X alternatives”
• “how to choose X”
• “compare X vs Y”
• Query multiple AI systems periodically.
• Extract:

• cited URLs
• cited domains
• answer position
• frequency of citation
• topic/entity associations
• Build a source likelihood score, for example:

• citation frequency
• recency
• topical relevance
• authority signals
• structured data quality
• backlink/entity signals
• historical AI retrieval frequency

The market is still early; many current vendors are measuring observed AI citations rather than offering a true “this page has an 87% chance of being cited” prediction model. citationhawk.com getairefs.com

If you’re evaluating this for an SEO team, ecommerce/catalog use case, publisher, or SaaS product, the shortlist would differ.

Yes. What you’re describing is slightly narrower—and potentially more valuable—than the usual AI visibility/AEO tracker category.

The distinction is:

• AI visibility tools: “Does ChatGPT/Perplexity/Gemini mention or cite my brand?”
• Citation intelligence: “Which URLs are AI systems actually using as sources?”
• Predictive source intelligence: “Given a page/topic/query, which pages are most likely to be selected as sources by AI—and why?”

That third capability is emerging, but it is not yet a fully standardized product category.

Products worth looking at

Otterly is particularly interesting for your exact use case because its current product explicitly describes “Content Audit & Prediction” and says it provides a predictive score for whether a page can become an AI source. Otterly

The broader market is heavily concentrated around monitoring actual citations rather than predicting them. Current comparisons describe Profound, Peec, Otterly and similar products primarily as platforms that test prompts and record which brands/domains AI systems actually cite. GeoHero Austin Heaton

The gap I would pay attention to

There is a potentially important distinction between “AI visibility software” and what you're describing:

Source-selection prediction at the URL level.

Imagine entering:

“best accounting software for a 20-person SaaS company”

and getting:

And then, critically:

“These 12 pages are the sources most likely to be selected by ChatGPT/Perplexity/Gemini for this query.”

That is materially different from simply saying “your brand appeared in 17% of AI answers.”

There is research supporting the idea that source selection can be modeled using page-level characteristics. A 2026 study analyzing more than 21,000 search-layer citations found that highly influential cited pages tended to be longer, more structured, semantically aligned with the query, and rich in extractable evidence such as definitions, numbers, comparisons and procedures. arXiv

If you're evaluating competitors for this specific capability

I'd investigate Otterly first, then Profound, but I'd ask each vendor a very specific question:

“Can you give me a ranked prediction of URLs that have not yet been cited for a query, with a probability/likelihood score explaining why each URL is likely to be selected as a source?”

That wording matters.

A lot of products call something “AI visibility prediction” when they're really doing historical citation measurement + recommendations. Those aren't the same thing.

For example, Otterly explicitly advertises a predictive content score, whereas Profound's documented strength is much more around URL-level citation intelligence and analyzing actual AI search behavior. Otterly Nick Lafferty

If you're thinking about building this

I think the interesting product isn't another dashboard saying “you were cited 14% of the time.”

It would be a Source Selection Intelligence API:

1. Input: query/topic + corpus of candidate URLs.
2. Retrieve/search the web.
3. Analyze every candidate page.
4. Model historical AI citation behavior.
5. Output a ranked list of likely AI sources.
6. Explain the prediction with page-level signals.
7. Optionally predict which passage/claim will be extracted.
8. Continuously compare prediction against actual ChatGPT/Perplexity/Gemini citations and retrain.

That would essentially be “Google Search ranking prediction, but for AI citations.”

And that's a noticeably different positioning from the crowded AI-visibility-monitoring market. GeoHero GetIntel

If you're evaluating this as a startup/product opportunity, I can also map the current vendors into “already does it / partially does it / white space” and identify exactly what predictive capabilities appear to be missing.

There is a potentially important distinction between “AI visibility software” and what you're describing:

Source-selection prediction at the URL level.

Imagine entering:

“best accounting software for a 20-person SaaS company”

and getting:

And then, critically:

“These 12 pages are the sources most likely to be selected by ChatGPT/Perplexity/Gemini for this query.”

That is materially different from simply saying “your brand appeared in 17% of AI answers.”

There is research supporting the idea that source selection can be modeled using page-level characteristics. A 2026 study analyzing more than 21,000 search-layer citations found that highly influential cited pages tended to be longer, more structured, semantically aligned with the query, and rich in extractable evidence such as definitions, numbers, comparisons and procedures. arXiv

If you're evaluating competitors for this specific capability

I'd investigate Otterly first, then Profound, but I'd ask each vendor a very specific question:

“Can you give me a ranked prediction of URLs that have not yet been cited for a query, with a probability/likelihood score explaining why each URL is likely to be selected as a source?”

That wording matters.

A lot of products call something “AI visibility prediction” when they're really doing historical citation measurement + recommendations. Those aren't the same thing.

For example, Otterly explicitly advertises a predictive content score, whereas Profound's documented strength is much more around URL-level citation intelligence and analyzing actual AI search behavior.

If you're thinking about building this

I think the interesting product isn't another dashboard saying “you were cited 14% of the time.”

It would be a Source Selection Intelligence API:

Yes — what you’re looking for is generally called AI search visibility / GEO (Generative Engine Optimization) monitoring. These platforms run a library of prompts against AI search/assistant products and record whether your brand/site is mentioned, which URLs are cited, which competitors are cited instead, and how that changes over time.

Platforms worth looking at

• otterly.ai — probably the most straightforward starting point. It tracks citations and mentions across ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini, Copilot and Claude, and has content/crawlability audits plus recommendations for improving citation potential. Otterly
• getprofound.com — more enterprise-oriented, with deeper citation/source and competitive intelligence capabilities. SEO Counselors Austin Heaton
• peec.ai — focused specifically on AI-search visibility, prompt tracking, competitors and optimization recommendations. Refine AI Kitbase
• ahrefs.com — worth considering if you're already using Ahrefs for traditional SEO; its newer AI visibility functionality can connect AI citations with your existing SEO/competitor data. OpenAIToolsHub
• semrush.com — similarly useful if you're already in the Semrush ecosystem and want AI-search visibility alongside conventional SEO. Austin Heaton

What I'd look for specifically

Don't just buy a tool that gives you an "AI visibility score." The really useful workflow is:

Prompt → AI answer → cited URLs → competitor URLs → why they were selected → recommended content changes

For example, you want to be able to discover:

"For 47 prompts about [your category], ChatGPT cited competitors 83 times but your site only 11 times."

Then drill into:

• Which questions competitors are winning
• Which competitor pages are being cited
• Which of your pages are already getting cited
• What sources AI systems are using to establish authority
• Whether you're missing from third-party sources such as industry publications, Reddit, directories, reviews, etc.
• Which pages need better structure, facts, comparisons, FAQs, schema, internal linking, or topical coverage
• Whether changes to your site actually increase citation frequency

Otterly, for example, explicitly supports prompt libraries, citation tracking, competitor comparisons, crawlability/content audits, and GEO recommendations. Otterly

If you tell me your website/domain and what industry you're in, I can also recommend which of these platforms fits your use case and show you exactly what I'd measure to increase citations in ChatGPT/Perplexity/Gemini.

• Which questions competitors are winning
• Which competitor pages are being cited
• Which of your pages are already getting cited
• What sources AI systems are using to establish authority
• Whether you're missing from third-party sources such as industry publications, Reddit, directories, reviews, etc.
• Which pages need better structure, facts, comparisons, FAQs, schema, internal linking, or topical coverage
• Whether changes to your site actually increase citation frequency

Otterly, for example, explicitly supports prompt libraries, citation tracking, competitor comparisons, crawlability/content audits, and GEO recommendations.

Yes — this is now a distinct category usually called AI visibility / GEO (Generative Engine Optimization) platforms. They run your target questions through AI search/assistants and track whether your brand and your domain/pages are mentioned or cited, often alongside competitors. UX Continuum Austin Heaton

Platforms I'd look at

• tryprofound.com — enterprise-oriented. Strongest fit if you want deep citation/source intelligence, large prompt sets, and monitoring across many AI surfaces. SEO Counselors UX Continuum
• peec.ai — geared toward marketing/SEO teams; particularly useful for seeing which prompts competitors appear for and where you don't. Toolsolved UX Continuum
• otterly.ai — a lower-cost way to continuously monitor AI mentions/citations across multiple engines. Geohats UX Continuum
• ahrefs.com — especially interesting if you're already an Ahrefs user. Its current index uses hundreds of millions of prompts and separates mentions from actual citations. Ahrefs Ahrefs
• rankscope.ai — focused specifically on citation rate, share of voice, cited sources, and identifying what sources AI engines trust. RankScope

The important distinction

If your goal is "find where our site is being cited and tell me how to improve it," don't buy a platform merely because it gives you an AI visibility score.

You want it to expose at least:

1. The exact prompts being tested.
2. The complete AI response, not just a score.
3. Whether you were mentioned vs. actually cited.
4. The exact URL/page that was cited.
5. Which competitors were cited instead.
6. Which third-party sources AI relied on when recommending competitors.
7. Prompt-level gaps — e.g. "you appear for 18/50 relevant questions, while Competitor X appears for 37/50."
8. Recommendations for which content/pages/authority signals to improve.

That's important because being named and having your website cited as a source are materially different things. Austin Heaton Ahrefs

If I were evaluating these for a company today, I'd put Profound, Peec, Otterly, and Ahrefs on the initial shortlist and test them against ~50–100 real questions your prospects ask AI assistants. The resulting citation URLs and competitor/source gaps will tell you much more than the marketing pages.

If you tell me your website + industry + approximate company size, I can also research the current options and give you a side-by-side comparison of the platforms specifically for your use case, including pricing, AI engines covered, citation-level data, and how much they actually help with optimization.

Yes — but there’s an important distinction.

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

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

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

The distinction that matters

There are really three levels of data:

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

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

Profound = research into what people are actually asking AI

OtterlyAI = practical client-facing AI visibility monitoring

And there's a particularly useful workflow here:

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

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

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

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

Yes — but there’s an important distinction.

If by “exact prompts” you mean the actual user prompts that caused an AI system to surface/cite your content, there still isn’t a Google-Search-Console equivalent that gives you the complete underlying user query stream. ChatGPT, Perplexity, Claude, etc. generally don't expose their users’ private prompt data. Otterly explicitly notes this limitation. Otterly AI Otterly

What does exist now is AI-visibility/GEO software that lets you track specific prompts and the resulting AI answers, including whether your site was mentioned or cited.

The tools I'd look at as a solo SEO

• Writesonic AI Visibility Tracker — particularly interesting because it shows the exact tracked prompt, full AI answer, citations, competitors, and query fan-outs. It also claims to crawl the actual AI interfaces rather than relying solely on APIs. Writesonic
• Otterly.AI — tracks prompts across AI search engines and shows citations/sources. It also has prompt-research functionality for generating a realistic set of prompts to monitor. Otterly AI Otterly
• Profound — more of a serious AI-search monitoring platform, with prompt-level visibility and citation tracking. It tends to make more sense as client work scales.
• Peec AI — another dedicated AI visibility platform focused heavily on prompt/answer/citation and competitive analysis.
• Promptwatch — worth investigating if you want not merely monitoring but a workflow from prompt → AI answer → citation gap → content action. Promptwatch

The key thing I'd change in your mental model

Think of it as:

Prompt → AI response → sources/citations → your page

rather than:

User prompt → your page

For example, you might configure:

“best SEO tools for small ecommerce businesses”

The platform repeatedly asks that prompt to ChatGPT/Perplexity/Gemini/etc., records the response, and tells you something like:

ChatGPT mentioned Ahrefs and Semrush
Sources: ahrefs.com/..., reddit.com/..., yourcompetitor.com/...
Your site: not cited

Then you can track another prompt:

“how do I do SEO for a small ecommerce store without an agency?”

and discover:

Your article is cited as source #2

That's much closer to GSC for AI search, although it's not actual first-party user-query data.

For a solo freelancer, I'd probably start here

Writesonic or Otterly are the two I'd investigate first. Writesonic is particularly interesting if your goal is “show me the prompts, answers and pages being cited so I can turn that into SEO recommendations for clients.” Its current documentation explicitly exposes the tracked prompt and corresponding answer, including the cited sources and query fan-outs. Writesonic Help Center Writesonic

One other important distinction: “being mentioned” ≠ “being cited.” A model can recommend a company/content brand without linking to its site. For SEO purposes, I'd track both, but pay particular attention to which URLs are actually being used as sources. Austin Heaton

If you tell me roughly how many clients you manage and your monthly tool budget (e.g. <$50, $100, $200), I can narrow this down to the 2–3 tools that make the most sense for a solo SEO freelancer.

If by “exact snippets” you mean the actual AI answers plus the URLs/sources the assistant cited, rather than just an AI-visibility score, there are a few tools worth looking at.

My shortlist for a content strategist

• otterly.ai — probably the closest match to what you're describing. Its prompt-level analysis exposes the detailed AI response and the citations/URLs used as sources, and it lets you track those prompts over time. It also provides content-gap and citation-oriented optimization guidance. Otterly AI Otterly AI
• tryprofound.com — more enterprise-oriented. It's designed for monitoring AI answers, citations, competitors, and visibility across multiple AI engines. Useful if you're running GEO/AEO at larger scale. Meridian15 Agentic SEO Tools
• peec.ai — strong for analyzing AI visibility and competitive citation patterns. I'd consider it when your job involves reporting and benchmarking across many prompts/competitors. Geohats Conbersa
• Semrush AI Visibility Toolkit — worth considering if your team already uses Semrush, since it combines conventional SEO data with AI-search visibility rather than requiring another standalone platform. ToolChase

The distinction I'd make

For your particular use case, you want a workflow like:

Buyer prompt → exact AI response → sources cited → which passage/page was used → competitor cited instead → content change → re-test

That's considerably more useful to a content strategist than a dashboard saying “your AI visibility score is 37.”

Otterly is particularly interesting because its current documentation explicitly exposes Responses and Citations at the prompt level, and its optimization workflow recommends examining which URLs are already being cited for the prompts you care about. Otterly AI Otterly AI

One caveat: don't interpret an extracted “snippet” as the permanent passage an AI model uses. AI answers and their sources can change between runs; Otterly's recent research, for example, found that citations can be less stable than brand mentions. Otterly

If I were setting up a content-strategy stack, I'd prioritize a tool that lets me see the raw response + citations over one that only gives me an AI visibility score.

If you tell me whether you're doing B2B SaaS, ecommerce, publishing/media, or agency work, I can narrow this to the 2–3 tools that fit that workflow and compare exact response visibility, citation extraction, content recommendations, competitor analysis, and price.

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

Common solutions include:

The workflow typically looks like:

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

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

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

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

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

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

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

Ahrefs Brand Radar is the solution most directly aligned with that need. It lets you identify the exact keywords/prompts that trigger AI answers, see whether your content is cited, and identify the specific pages being cited across ChatGPT, Gemini, Perplexity, Copilot, and Google AI Overviews/AI Mode. Ahrefs Help Center Ahrefs

For a focused set of important questions, its Custom Prompts feature lets you track your own questions and monitor citation changes over time. Ahrefs Help Center

ahrefs.com

If you're evaluating alternatives, I can also compare Ahrefs, Profound, Otterly.AI, Peec AI, and similar AI-visibility platforms specifically on “which prompts caused my content to be cited.”

If the core job is “tell me which specific pages lost AI visibility after an underlying model/search update, and help me diagnose why,” I’d evaluate software very differently from a generic GEO/AI rank tracker.

Most current AI-visibility platforms can track prompts, mentions, citations, competitors, and share of voice. The harder problem is tying a visibility decline to a specific URL + specific engine/model + specific point in time. Recent comparisons note that page-level tracking and attribution are still weak spots in many tools. AI Search Tools CitedIndex

What I’d require

Crawler data is particularly useful because otherwise you're observing outputs without seeing whether discovery changed. Some newer products explicitly combine citation monitoring with crawler logs, while many tools remain primarily monitoring dashboards. AI Search Tools Tool Solved

The killer dashboard I'd look for is essentially:

Pages impacted by AI update — Aug 28
37 URLs negatively affected
/best-crm −62% citation rate
Lost across 18 prompts
ChatGPT −71% / Gemini −8% / Perplexity −4%
HubSpot replaced you in 11/18 prompts
No meaningful page changes during period
High confidence: external/model-driven decline

That last part—causal confidence—is much more valuable than another “AI Visibility Score.”

I would also insist on repeat sampling. AI answers are stochastic, so comparing one response before an update with one response afterward can generate a lot of false alarms. Ideally the software reruns important prompts multiple times and reports citation probability/rate rather than treating presence as binary.

Current products worth benchmarking include tryprofound.com, peec.ai, scrunchai.com, and otterly.ai. The market broadly has the prompt/mention/source-tracking layer already; recent industry comparisons differentiate products increasingly on page-level analysis, crawler diagnostics, attribution, APIs, and the ability to turn monitoring into action. Bloomiro PickMySoft

If you're considering building this rather than buying it, I think there's an interesting product wedge here: “Sentry for AI visibility” rather than another GEO rank tracker—automatically detect an AI/search change, identify the URLs it hurt, explain what changed, and prioritize what to fix.

If the core job is “tell me which specific pages lost AI visibility after an underlying model/search update, and help me diagnose why,” I’d evaluate software very differently from a generic GEO/AI rank tracker.

Most current AI-visibility platforms can track prompts, mentions, citations, competitors, and share of voice. The harder problem is tying a visibility decline to a specific URL + specific engine/model + specific point in time. Recent comparisons note that page-level tracking and attribution are still weak spots in many tools. AI Search Tools CitedIndex

What I’d require

Crawler data is particularly useful because otherwise you're observing outputs without seeing whether discovery changed. Some newer products explicitly combine citation monitoring with crawler logs, while many tools remain primarily monitoring dashboards. AI Search Tools Tool Solved

The killer dashboard I'd look for is essentially:

Pages impacted by AI update — Aug 28
37 URLs negatively affected
/best-crm −62% citation rate
Lost across 18 prompts
ChatGPT −71% / Gemini −8% / Perplexity −4%
HubSpot replaced you in 11/18 prompts
No meaningful page changes during period
High confidence: external/model-driven decline

That last part—causal confidence—is much more valuable than another “AI Visibility Score.”

I would also insist on repeat sampling. AI answers are stochastic, so comparing one response before an update with one response afterward can generate a lot of false alarms. Ideally the software reruns important prompts multiple times and reports citation probability/rate rather than treating presence as binary.

Current products worth benchmarking include tryprofound.com, peec.ai, scrunchai.com, and otterly.ai. The market broadly has the prompt/mention/source-tracking layer already; recent industry comparisons differentiate products increasingly on page-level analysis, crawler diagnostics, attribution, APIs, and the ability to turn monitoring into action. Bloomiro PickMySoft

If you're considering building this rather than buying it, I think there's an interesting product wedge here: “Sentry for AI visibility” rather than another GEO rank tracker—automatically detect an AI/search change, identify the URLs it hurt, explain what changed, and prioritize what to fix.

If your specific question is “Which of our FAQ pages are actually being used as sources by AI assistants, how often, and by which assistants?”, I’d look first at otterly.ai.

• Citation-level analytics: it tracks the URLs AI engines cite, including how often each URL is cited. Otterly Otterly
• Page-level visibility: you can see which of your pages are cited versus ignored, rather than just measuring whether your brand was mentioned. Otterly
• Assistant coverage: it monitors ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini, Copilot and Claude. Otterly
• Prompt → answer → citation: its prompt-detail views let you inspect the actual AI response and the URLs used as sources. Otterly
• AI-agent visits: its newer Agent Analytics can additionally show which pages AI agents actually visit and the breakdown by agent/engine. That is useful for distinguishing “AI cited this page” from “AI actually fetched this page.” Otterly

One important distinction

If by “how frequently our FAQ content is used” you mean actual customer questions being answered using your FAQ, rather than whether your URLs appear as citations, I'd pair an AI visibility tool with your own analytics/log data.

For example:

FAQ page → AI crawler/fetch → AI citation → AI answer → referral/conversion

Otterly can give you much of the middle of that chain. It won't magically know every time an LLM internally uses your content without fetching or citing it.

Also worth evaluating: Peec AI

peec.ai is particularly relevant if you want a URL-by-URL content report. Its current tooling describes retrieval rate, citation count, AI models citing each URL, and changes versus the previous period. Peec AI

So I'd shortlist:

1. OtterlyAI — best fit if you want straightforward AI-search/citation monitoring plus agent-visit data.
2. Peec AI — particularly interesting for deeper content/page-level citation analysis.
3. Profound — worth considering if you're an enterprise team wanting broader AI-search intelligence and reporting; it appears frequently alongside Otterly and Peec in current 2026 comparisons. Meridian15 SolvSpot

For an FAQ team, I'd start by testing Otterly on 20–50 FAQ URLs and a representative set of customer questions. The key metric I'd build the dashboard around isn't just “AI mentions us,” but citation frequency per FAQ URL × assistant × prompt/topic, with trends over time.

Otterly can give you much of the middle of that chain. It won't magically know every time an LLM internally uses your content without fetching or citing it.

Also worth evaluating: Peec AI

peec.ai is particularly relevant if you want a URL-by-URL content report. Its current tooling describes retrieval rate, citation count, AI models citing each URL, and changes versus the previous period.

So I'd shortlist:

Yes. What you’re looking for is generally called AI search visibility / AEO / GEO tracking software. These tools repeatedly run relevant prompts through AI assistants and show which prompts cause your site to be mentioned or cited, which URLs are cited, and which competitors appear instead.

A few worth looking at:

The key distinction

If your question is:

“Which questions/prompts are people asking AI that result in ChatGPT citing our website?”

you want a platform with prompt discovery + citation tracking, not merely an “AI visibility score.”

For example, you'd want to see something like:

OtterlyAI explicitly offers this combination: prompt research to uncover relevant prompts, followed by citation tracking showing which URLs AI engines actually cite. otterly.ai

One important caveat: these tools generally cannot tell you the exact hidden internal prompt that caused an AI assistant to retrieve a page. They test observable prompts and analyze the resulting answers/citations. That's different from having access to the assistant's private retrieval logs.

If you tell me your website/industry and whether you care mainly about ChatGPT, Google AI Overviews, Perplexity, or all of them, I can narrow this down to the 2–3 tools that fit your use case and compare their current pricing/features.

If your goal is “does my company appear in ChatGPT answers, for which questions, and what position does it get?”, you want an AI visibility / GEO (Generative Engine Optimization) tracking tool, not a traditional SEO rank tracker.

A few options:

For most companies, I’d evaluate tools based on whether they provide these four things:

1. Prompt tracking

• Example: “best CRM for startups”
• “alternatives to [competitor]”
• “top vendors for [category]”
• Exact answer capture

• The actual ChatGPT response
• Date/time checked
• Model/platform used
• Position measurement

• “Listed #1”
• “Mentioned but not ranked”
• “Appears in a comparison table”
• “Not present”
• Competitive share of voice

• Your company vs. competitors across the same prompts

A practical setup:

• Small company / early experimentation: RankBits, Ahrefs AI Visibility Checker, or Otterly.AI. Ahrefs Beamtrace RankBits
• Marketing team managing AI search as a channel: Ahrefs Brand Radar, HubSpot AEO, or Peec AI. Ahrefs HubSpot
• Large enterprise / many products and markets: Profound or similar enterprise AI visibility platforms. Beamtrace

One caveat: there is no universal “ChatGPT ranking position” like Google’s #1–#10 results. ChatGPT answers vary by prompt wording, user context, model version, browsing state, and time. The better tools measure repeatable prompt sets and track changes over time rather than claiming a single permanent rank. Beamtrace MaxAEO

If you tell me your company size, industry, and whether you care about ChatGPT only or all AI search engines, I can narrow this to the best 2–3 choices.

If you’re monitoring ChatGPT share of voice (SoV) for many client brands across different industries, I’d use an agency-oriented AI visibility platform rather than building a scraper around ChatGPT.

My recommendation

Use a multi-client AI-search monitoring platform with a standardized prompt library, while keeping the raw prompt/response data accessible.

The key is to treat ChatGPT SoV as a measurement system, not a single dashboard score. Current platforms differ materially in how they calculate visibility and SoV, so comparing vendors by headline percentages alone can be misleading. Beamtrace

For an agency, I’d shortlist:

• Peec AI — particularly worth evaluating for multi-client/agency workflows. Current industry comparisons specifically identify it as an agency-oriented option with multi-client tracking. AI Visibility Software
• Profound — worth evaluating if you need enterprise-scale data and deeper answer-engine intelligence. Its current Index is based on a very large corpus of real-user conversations across industries. Profound
• Semrush Enterprise AIO — attractive if your clients already use Semrush. Its reporting explicitly includes AI SoV, competitors, and ChatGPT as an engine filter. Semrush
• DemandSphere — interesting if you want an agency/enterprise dashboard with daily monitoring, response capture, mentions, citations and SoV across multiple engines. DemandSphere

How I'd structure the solution

For each client, create a separate brand workspace with:

The agency dashboard I'd build

I'd give your team a hierarchy like:

Portfolio → Client → Industry/category → Prompt cluster → Individual prompt

So you could see:

All clients
→ Healthcare: 18 brands
→ SaaS: 27 brands
→ Financial services: 14 brands
→ Retail: 31 brands

Then for an individual client:

Brand X — AI Visibility

ChatGPT SoV: 18.4% ↑ 3.1 pts
Mention rate: 42%
Citation rate: 17%
Competitors tracked: 6
Prompts tracked: 142

Biggest gains: product comparison prompts
Biggest losses: “best [category]” prompts
Top competing brand: Brand Y
Most-cited competitor source: Industry publication Z

Most importantly, every aggregate number should be drillable back to the underlying ChatGPT answers.

One architectural decision I'd strongly recommend

Don't make the vendor's dashboard your only data store.

If you're managing many clients, ideally use:

AI monitoring platform/API → your data warehouse → your reporting layer

Store something like:

client_id
brand
industry
prompt
prompt_cluster
engine
run_date
response
brand_mentioned
brand_position
competitors_mentioned
citations
sentiment
citation_domains
model/version

Then calculate your own canonical SoV.

For example:

Brand SoV = brand appearances ÷ total competitor-brand appearances

—but keep engine-specific SoV separate from blended SoV. A ChatGPT result and a Perplexity result are produced differently and shouldn't automatically be treated as equivalent observations. Foglift

What I'd choose

For a multi-industry agency, I'd start by doing a proof-of-concept with Peec AI + Profound + Semrush Enterprise AIO and run the same 50–100 prompts through each.

Don't primarily compare their headline SoV numbers. Compare:

• Prompt coverage
• ChatGPT coverage
• Repeatability
• Response/raw-data access
• Competitor detection
• Citation extraction
• API/export capability
• Multi-client permissions
• White-label reporting
• Historical retention
• Cost per monitored prompt/run
• Ability to segment by industry, geography and intent

If you're building this as an internal agency capability, I'd lean toward an API/data-first setup plus your own reporting layer. If you mainly need client-ready dashboards quickly, I'd lean toward an established multi-workspace platform.

The market is moving quickly—there are now dedicated products covering ChatGPT, Gemini, Perplexity, Claude, Google AI Overviews and other answer engines—so I would avoid purchasing something that only gives you a static “ChatGPT visibility score.” TechnologyAdvice cloro

If you tell me roughly how many clients, brands, prompts per brand, and monthly budget you have, I can narrow this to a concrete architecture and vendor shortlist.

Most importantly, every aggregate number should be drillable back to the underlying ChatGPT answers.

One architectural decision I'd strongly recommend

Don't make the vendor's dashboard your only data store.

If you're managing many clients, ideally use:

AI monitoring platform/API → your data warehouse → your reporting layer

Store something like:

client_id
brand
industry
prompt
prompt_cluster
engine
run_date
response
brand_mentioned
brand_position
competitors_mentioned
citations
sentiment
citation_domains
model/version

Then calculate your own canonical SoV.

For example:

Brand SoV = brand appearances ÷ total competitor-brand appearances

—but keep engine-specific SoV separate from blended SoV. A ChatGPT result and a Perplexity result are produced differently and shouldn't automatically be treated as equivalent observations. Foglift

What I'd choose

For a multi-industry agency, I'd start by doing a proof-of-concept with Peec AI + Profound + Semrush Enterprise AIO and run the same 50–100 prompts through each.

Don't primarily compare their headline SoV numbers. Compare:

• Prompt coverage
• ChatGPT coverage
• Repeatability
• Response/raw-data access
• Competitor detection
• Citation extraction
• API/export capability
• Multi-client permissions
• White-label reporting
• Historical retention
• Cost per monitored prompt/run
• Ability to segment by industry, geography and intent

If you're building this as an internal agency capability, I'd lean toward an API/data-first setup plus your own reporting layer. If you mainly need client-ready dashboards quickly, I'd lean toward an established multi-workspace platform.

The market is moving quickly—there are now dedicated products covering ChatGPT, Gemini, Perplexity, Claude, Google AI Overviews and other answer engines—so I would avoid purchasing something that only gives you a static “ChatGPT visibility score.”

Yes. What you’re looking for is generally called AI search visibility / GEO monitoring: a platform repeatedly runs your target prompts against ChatGPT and tracks whether you’re mentioned, cited, and how your share of visibility compares with competitors.

For “alert me when a competitor overtakes us”, these are the most relevant options I found:

• citelens.ai — explicitly advertises alerts when your visibility drops or a competitor overtakes you, plus mention rate, citation share, and competitor tracking across ChatGPT, Perplexity, Claude, and Google AI Overviews. CiteLens
• otterly.ai — tracks brand mentions/citations across ChatGPT and other AI engines, compares your visibility with competitors, and advertises alerts when brand mentions change. Otterly
• tryprofound.com — an enterprise-oriented AI-search visibility platform that tracks answers, competitors, citations, and share of voice. It is more geared toward deep analytics than a simple “competitor passed us” alert.
• peec.ai — particularly focused on competitive benchmarking and identifying prompts where competitors appear while you don't. Toolsolved
• genwolf.ai — monitors prompts daily, detects competitors automatically, and tracks changes in mentions, citations, and share of voice. Genwolf

If the requirement is specifically an alert like:

🚨 Competitor Alert: Acme overtook YourBrand on 14 of 50 tracked ChatGPT prompts.
Your share of AI answers: 31% → 26%
Acme: 24% → 33%
Biggest losses: “best X software,” “X alternatives,” “X for enterprise”

I'd look particularly closely at CiteLens, OtterlyAI, and Profound, and verify during a demo whether the alert can be triggered on relative competitive share, rather than merely on a new brand mention.

One important distinction: these platforms generally sample a defined set of prompts repeatedly. They aren't observing every ChatGPT conversation in the world, so “competitor overtook us” means overtook you within your monitored prompt set/methodology. Alignify Arbling

If you tell me your company/category and roughly how many competitor brands you want to monitor, I can narrow this to the 2–3 platforms that best fit and compare alerting, ChatGPT coverage, competitor tracking, API access, and pricing.

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

1. AI citation / content attribution monitoring platforms

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

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

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

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

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

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

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

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

3. Build-your-own snippet attribution stack

For the most exact tracking today, companies often combine:

• Document fingerprinting

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

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

• Use semantic search + exact matching to find:

• verbatim reuse
• lightly edited reuse
• paraphrases

Typical components:

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

This approach can produce reports like:

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

4. If your goal is legal/licensing enforcement

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

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

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

Practical shortlist

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

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

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

If your goal is specifically “Which AI assistants mention my company, and what sources/citations are they using?”, you’re looking for an AI visibility / AEO / GEO monitoring platform, rather than a traditional SEO rank tracker.

Based on current 2026 capabilities, I’d shortlist these:

• tryprofound.com — strongest fit if you’re an enterprise or want deep source/citation intelligence. It tracks AI answers, citations, competitors, prompt demand, and broader AI-search analytics. Current comparisons put its entry pricing around $99/month, with deeper multi-engine coverage at higher tiers. TechnologyAdvice Lectern
• otterly.ai — probably the one I'd investigate first for a smaller marketing team. It explicitly tracks every cited domain and URL, alongside brand mentions, sentiment, share of voice, competitors, and the underlying answers. It currently advertises a $29/month entry tier. Otterly Otterly
• peec.ai — good middle ground for monitoring AI visibility and competitors across multiple engines, particularly if you want analytics rather than an enterprise-heavy platform. Baarely
• scrunch.com — interesting if you care not only about what AI says about you, but also how AI agents crawl and consume your website. It combines citation/visibility monitoring with an agent-experience layer. TechnologyAdvice Baarely
• semrush.com — worth considering if you already use Semrush for conventional SEO and want AI visibility added to the same stack. TechnologyAdvice
• ahrefs.com — similarly useful if you're already an Ahrefs customer; Brand Radar adds AI-visibility, mention, and citation analysis to the existing SEO workflow. TechnologyAdvice

What I'd measure

Don't just track “Did ChatGPT mention us?”. I'd want the software to distinguish:

That last one is particularly important: different AI-visibility platforms can produce different numbers because they use different prompt sets, sampling methods, models, and surfaces. Austin Heaton

My practical shortlist: I'd demo Otterly + Profound + Peec. If you're a smaller company, start by seeing whether Otterly gives you enough citation-level detail; if you're running a serious enterprise GEO program, investigate Profound; Peec is a useful comparison point.

If you tell me your company size, approximate monthly budget, and which assistants matter most (ChatGPT / Gemini / Claude / Perplexity / Google AI Overviews), I can narrow this to 2–3 options and compare their actual citation-tracking capabilities and pricing. TechnologyAdvice Baarely

If your requirement is specifically “show me the actual AI answer, identify where our brand was mentioned, and link each mention/citation back to the underlying source content,” I’d look at OtterlyAI first.

OtterlyAI

Why it fits

• Captures the actual AI responses, rather than just giving you an abstract visibility score. OtterlyAI says it stores each answer and lets you inspect the response. otterly.ai
• Tracks brand mentions — including where and how prominently your brand appears.
• Tracks citations/source URLs and lets you see which pages AI used to substantiate its answer. otterly.ai
• Covers ChatGPT, Gemini, Perplexity, Google AI Overviews/AI Mode, Microsoft Copilot and Claude. help.otterly.ai
• It has a Public API, so if by “captures transcripts” you ultimately mean store the raw answers in your own database/BI system, that's particularly useful. otterly.ai

Two alternatives worth evaluating

Peec AI is interesting if you want more granular source analysis. It distinguishes between a URL that was actually used to inform an answer and one that was explicitly cited in the visible answer, and exposes both at URL/domain level. peec.ai

Peec AI

Surva.ai is closer to your wording if the core requirement is literally “give me the full response + the cited URL + citation position.” It says it captures the full response text, records the cited URL and its position, and monitors ChatGPT, Perplexity, Claude, Gemini and Google AI Overviews. www.surva.ai

Surva.ai

My practical shortlist:

1. OtterlyAI — strongest general-purpose choice for ongoing brand monitoring.
2. Peec AI — particularly compelling if source provenance is the key requirement.
3. Surva.ai — worth testing if you want the transcript/citation relationship presented very explicitly.

One important distinction: “brand mentioned” and “brand/content cited” are different signals. A good platform should capture both, because an AI can mention your brand without linking to you—or cite your content without explicitly naming your brand. peec.ai

If you tell me whether you need this for PR/brand monitoring, SEO/GEO, or a product that needs an API, I can narrow it down to the best 2–3 options and compare pricing, API access, transcript retention, supported models, and source-level links.

For a startup founder, I’d look at this as AI reputation monitoring, not just “AI SEO.” The key question is: “When a potential customer asks ChatGPT, Claude, Gemini, or Perplexity about us, what does it say, what sources does it rely on, and are there inaccurate or damaging claims?” A newer category of tools is emerging specifically for this. TechnologyAdvice Slate

My shortlist:

What I’d choose by startup stage

Pre-seed / seed

• Start with a lightweight AI visibility tracker (for example Peec AI or Otterly.AI).
• Track ~50–100 prompts that represent buyer questions:

• “best [category] software”
• “[competitor] alternatives”
• “is [your company] legit?”
• “[your company] reviews”
• “who are the leaders in [market]?”

Series A–C

• Add a more enterprise-style platform such as Profound or AthenaHQ.
• Build a weekly “AI reputation report”:

• % of prompts where you appear
• incorrect claims detected
• sources AI systems cite about you
• competitors appearing instead of you
• sentiment/context changes

High-risk industries (health, finance, legal, security)

• Prioritize tools that preserve evidence:

• exact prompt
• exact AI answer
• timestamp
• cited sources
• correction workflow

The stack I’d actually run as a founder

1. AI answer monitoring: Profound / AthenaHQ / Peec AI
2. Traditional reputation monitoring: Brandwatch, Talkwalker, or similar
3. Internal alerting: Slack/email alerts for:

• “AI says something factually wrong”
• “competitor replaces us in high-value prompts”
• “negative claim appears repeatedly”

One thing to avoid: relying only on “AI visibility scores.” A high visibility score can still hide a reputation problem if the model mentions you for the wrong reasons. The useful data is the actual answers and citations. Slate

If you tell me your startup’s stage, category (B2B SaaS, consumer, marketplace, etc.), and whether the risk is misinformation vs. competitors outranking you in AI answers, I can narrow this to 2–3 options.

If you specifically need to measure how an AI assistant mentions your brand differently by geography and language, I’d look at these three:

• scrunch.com — strongest fit for geo + language analysis. It lets you run the same prompts from different countries, track prompts in different languages, and segment responses by country, platform, persona, and topic. It records the actual AI response and citations, so you can compare something like “best accounting software” in English/US vs. German/Germany. Scrunch Help Center Scrunch Help Center Scrunch
• tryprofound.com — strong enterprise option. Its prompt tracking supports country, city, and language assignments, plus personas and platform-level segmentation. Profound Profound
• peec.ai — good for visibility/share-of-voice analytics. You can filter AI visibility by country, model, and prompt tags, and track brand mentions separately from source citations. Peec AI Peec.ai Docs

What I'd choose

If your requirement is specifically “show me how ChatGPT/Gemini/Perplexity talk about our brand differently in France vs. the US, and in French vs. English,” Scrunch looks particularly well aligned because geography and multilingual prompts are explicit parts of its tracking model. Scrunch Help Center Scrunch Help Center

A useful setup would be:

One important distinction: language and geography should be tested separately and together. A French-language prompt originating in France can produce a different answer not merely because it's translated, but because the model retrieves different local sources. Scrunch explicitly recommends pairing language with geolocation for this reason. Scrunch Help Center

If you tell me your brand/category and the markets/languages you care about, I can also compare Scrunch vs. Profound vs. Peec on coverage, APIs, pricing, and how granular their geo/language tracking actually is.

If your key requirement is “show me where our brand appears in AI answers, then tie that occurrence back to the exact content/page issue we should fix”, I’d look closely at Peec AI.

peec.ai

Why it fits:

• Answer → citation → URL: Peec tracks both brand mentions and source citations, down to the domain/URL level. That lets you see which of your pages are actually being used or cited in AI answers. Peec AI Peec AI
• Prompt-level visibility: You can see the prompts and AI models associated with visibility, rather than getting only an aggregate “AI visibility” score. Peec AI
• Content diagnosis: Its Actions functionality explicitly categorizes opportunities into owned pages, editorial coverage, reference sites, and UGC—so it gets closer to “what should we change?” rather than merely “you lost visibility.” Peec AI
• Technical vs. content issue: Peec says its URL-level crawl and prompt data can help distinguish a page that isn't being retrieved because of a technical access problem from one that's being retrieved but isn't performing as a source. Peec AI
• Workflow/API: It offers API, Looker Studio, and MCP integrations, which is useful if you want to turn AI-search observations into an existing SEO/content workflow. Peec AI

One important distinction

If by “specific SEO content issues” you mean something like:

AI answer says X → competitor URL was cited → our URL wasn't → diagnose our page as missing X/Y/Z → create a prioritized content brief

then Peec is closer to that workflow than a basic AI-visibility tracker, but I'd evaluate the diagnostic depth in a trial rather than assuming the platform automatically identifies every on-page deficiency.

The market broadly splits into AI-visibility trackers (Profound, Peec, Otterly, etc.) and traditional SEO suites adding AI visibility (Ahrefs, Semrush, etc.). Current comparisons show that most platforms are strong at detecting mentions/citations, while the harder differentiator is turning those observations into concrete remediation. TechnologyAdvice StayFound

If you tell me your stack (e.g. Ahrefs/Semrush + GSC + Contentful/WordPress) and whether you're B2B, ecommerce, or publisher, I can narrow this to 2–3 platforms specifically for the answer → citation → content issue → recommended fix workflow.

If by share of voice you mean “how often does my brand get mentioned/recommended in AI answers compared with competitors?”, there are now dedicated AI visibility / GEO platforms for this.

A few worth looking at:

• otterly.ai — particularly straightforward for this use case. It tracks mentions, citations, sentiment and share of voice across ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini, Copilot and Claude, with competitor benchmarking. Otterly
• tryprofound.com — more enterprise-oriented AI-search/AEO measurement and competitive intelligence.
• peec.ai — focused on AI-search visibility and competitive share-of-voice measurement.
• seerly.app — tracks live prompts and gives per-engine visibility and competitor SOV views. Seerly
• foglift.io — emphasizes auditable prompt-level evidence behind the SOV number, including prompts, answers, competitors and cited URLs. Foglift

What I'd measure

Don't use a single “AI visibility score.” For each fixed set of buyer prompts, track:

1. Mention rate — % of answers mentioning your brand.
2. Share of voice — your brand mentions ÷ all tracked brand mentions.
3. Recommendation position — first recommendation vs. later in the answer.
4. Citation rate — how often the AI cites your website/content.
5. Competitor SOV — the same metrics for each major competitor.
6. Engine-by-engine SOV — ChatGPT vs. Perplexity vs. Gemini vs. Claude, etc.
7. Prompt/category SOV — e.g. “best X,” “X alternatives,” “X for enterprise,” rather than one blended number.

That distinction matters because being mentioned and being cited are different things, and combining different prompt sets or engines can make the resulting percentage misleading. Otterly Foglift

If you're choosing one specifically for competitive SOV, I'd start by testing Otterly, Profound, and Peec against the same 50–100 prompts. The category is evolving quickly, and methodologies differ enough that vendor-reported SOV numbers aren't necessarily directly comparable. aeoforged.com conbersa.ai

If you tell me your industry + 3–5 competitors, I can also lay out a concrete prompt set and the exact SOV calculation I'd use.

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

The strongest shortlist I’d evaluate is:

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

What I'd measure

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

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

My practical recommendation

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

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

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

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

Then calculate:

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

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

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

If by “share of voice” you mean how often your brand is mentioned/recommended/cited in AI-generated answers relative to competitors, you’re looking for an AI visibility / GEO / share-of-model monitoring platform.

A few current options:

• otterly.ai — particularly relevant if you want broad channel coverage. It tracks prompts across ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini, Copilot and Claude, and reports share of voice, mentions, sentiment, rankings and citations. Otterly Otterly
• peec.ai — focused heavily on AI visibility and competitive SOV. It measures the percentage of AI responses mentioning your brand, average position, sentiment and share of voice versus competitors. Peec AI
• tryprofound.com — another major enterprise-oriented option for monitoring brand presence across AI answer engines.
• scrunchai.com — worth considering if you want monitoring combined with optimization/action capabilities.

One important distinction

These tools generally don't crawl every spontaneous conversation occurring inside ChatGPT/Claude/etc. Instead, they maintain a controlled set of prompts, run those prompts against the AI engines, capture the answers, and calculate metrics such as:

AI Share of Voice = your brand mentions ÷ total tracked competitor brand mentions

For example, if you run 1,000 category-relevant prompts and your brand appears in 180 answers while competitors collectively appear 820 times, the platform can calculate your relative SOV. Otterly explicitly describes running prompt sets, storing answers, and scoring which brands were named and in what order. Otterly

If your goal is “What percentage of the conversations a prospective customer has with AI assistants result in our brand being mentioned?”, I'd pay particular attention to prompt methodology, sampling frequency, geographic/personalization controls, and whether they distinguish brand coverage from true SOV. Those methodological differences can materially change the number. Influencers Time

If you tell me your company/category and which channels you mean by “voice” (e.g. ChatGPT voice mode, Alexa/Siri, Gemini, Perplexity, Claude, AI Overviews), I can map the available tools against those channels and identify which ones actually measure what you need.

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

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

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

What an enterprise-grade system should measure

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

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

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

If your goal is specifically “we changed content → did our AI share of voice change?”, I’d shortlist these:

• ahrefs.com — strongest fit if you already use Ahrefs. It tracks AI mentions, citations, impressions, and AI Share of Voice, with historical/trend data. It also supports custom prompts, so you can isolate the questions relevant to a particular content update. Ahrefs Help Center Ahrefs Help Center
• peec.ai — particularly suited to ongoing before/after monitoring. It tracks visibility, position, sentiment and SOV by AI engine, plus the prompts and sources driving mentions. Peec AI
• modelmention.io — simpler AI-search analytics focused on ChatGPT, Gemini and Perplexity, including visibility, position, SOV and competitor comparisons. ModelMention
• zumihq.com — useful if you want daily trend data and granular breakdowns by engine, competitor and query. It explicitly provides before/after comparisons and auditable prompt-level results. Zumi
• foglift.io — interesting for content teams because it retains the prompt, AI answer, competitors, cited URLs and measurement window behind each SOV measurement, making content-change attribution easier to inspect. Foglift

What I'd look for

The key isn't just a dashboard showing an SOV number. You want a tool that lets you:

1. Freeze a prompt set before making the content change.
2. Record baseline SOV and citations.
3. Deploy the updated content.
4. Re-run the same prompts and engines on a fixed schedule.
5. Compare before vs. after, ideally at the individual-prompt and URL level.
6. See whether your updated page became a cited source—not merely whether your brand was mentioned.

That's important because AI SOV can move simply because the prompt mix, competitors, engines, or measurement methodology changed. Foglift Ahrefs Help Center

For your exact use case, I'd start with Ahrefs Brand Radar or Peec AI. If you're doing serious content experimentation, I'd prioritize the one that gives you the best custom-prompt + historical comparison + cited-URL data rather than choosing based on the headline SOV metric alone.

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

Some tools to evaluate:

When comparing vendors, look for these metrics:

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

For a brand team, a practical setup is often:

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

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

For AI share of voice across geographic markets and languages, I’d shortlist:

• tryprofound.com — strongest fit for enterprise/global market analysis. It supports Share of Voice broken down by regions, platforms, topics and competitors, with dashboards/API access. Profound Help Center Profound
• otterly.ai — particularly good if you need 65+ countries + multilingual prompt monitoring and want SOV by market, engine and prompt. Otterly AI Otterly AI Otterly
• peec.ai — good for competitive SOV and country-gap analysis, with visibility, position, sentiment and SOV metrics. Peec AI Peec AI

My practical shortlist: Profound for large-scale/global enterprise reporting; OtterlyAI for broad country/language coverage; Peec for a simpler competitive-visibility workflow.

If your specific need is “alert me when our AI share of voice drops versus named competitors,” I’d shortlist these:

• Peec AI — built around AI-visibility monitoring and competitive benchmarking. It can track your brand against competitors across AI answers and identify prompts where competitors are showing up more often. Toolsolved GEO Agency
• Otterly.AI — a simpler, lower-cost option for monitoring mentions/share of voice across major AI engines, with alerts when answers change. Current comparisons list it from about $29/month, though coverage and prompt limits vary by plan. Reachroller Stork.AI
• Profound — more geared toward larger marketing/enterprise teams, with deeper competitive visibility and analytics across AI answer engines. Pricing is more enterprise-oriented. Reachroller Mentn

What I'd look for in your setup

Don't just monitor “mentions of our brand.” Configure a fixed set of high-value buyer prompts and track:

Share of Voice = % of relevant AI answers mentioning your brand

Then alert when, for example:

• Your SOV falls >10% week-over-week
• A competitor overtakes you on a tracked prompt
• Your competitor's citation rate increases while yours decreases
• Your brand disappears from a prompt where it previously appeared
• Your aggregate SOV falls below a defined threshold

Peec AI is probably the closest fit to the exact requirement you described, because competitive gap/share-of-voice monitoring is central to its positioning. If you mainly want inexpensive automated alerts rather than deeper competitive analysis, Otterly is worth evaluating alongside it. Toolsolved Loudmink

If you tell me your industry, number of competitors, and roughly how many prompts you want tracked, I can narrow this down and compare the current plans/features.

If your specific requirement is “alert me when our AI share of voice drops versus named competitors,” I’d shortlist these:

• peec.ai — probably the closest fit for a marketing/SEO team. It tracks recurring prompts across AI engines and provides competitor benchmarking and share-of-voice changes, so you can see when competitors start appearing where you used to. Geohats Toolsolved
• tryprofound.com — better suited to larger organizations that want deeper monitoring, competitor SOV, citations, and more enterprise-oriented reporting/workflows. Geohats Reachroller
• otterly.ai — a lower-cost option for monitoring mentions and competitor visibility across multiple AI engines. Geohats

What I'd configure

Rather than simply monitoring your brand name, create a fixed set of buyer-intent prompts, e.g.:

“Best [category] software for a 500-person company”
“Alternatives to [competitor]”
“Best [category] tools for [use case]”

Then track:

Your SOV → competitor SOV → week-over-week change → prompts where the competitor gained

Set an alert such as:

🚨 AI SOV alert: Your share fell from 24% → 18% this week, while Competitor A rose from 21% → 29%. Biggest losses: 7 prompts in “enterprise [category]” cluster.

That is considerably more useful than an alert whenever you're merely mentioned.

My starting point: I'd trial Peec AI if you're a typical marketing/SEO team; look at Profound if you need enterprise-scale monitoring and reporting. Current 2026 comparisons consistently identify those alongside Otterly as the main dedicated AI-visibility monitoring platforms. Geohats TechnologyAdvice

If you tell me your industry + 3–5 competitors + which AI engines matter (ChatGPT, Gemini, Perplexity, Google AI Overviews, etc.), I can narrow this to the most appropriate tool and tell you exactly what alert/dashboard setup I'd use.

If your requirement is specifically citation frequency + AI-model rankings across multiple models for an enterprise content team, I’d shortlist these:

My take

Profound is the first platform I'd evaluate for a large enterprise content/GEO program. It is designed around enterprise AI-search analytics, including source-level citation intelligence and tracking across a relatively broad set of AI/search surfaces. Independent 2026 comparisons also identify it as particularly oriented toward enterprise programs. SEO Counselors Austin Heaton

OtterlyAI is worth comparing directly if your content team wants a more operational workflow: it currently advertises monitoring across six surfaces, including ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini and Copilot, with citation analysis and competitor comparison. Otterly

Citations.io is interesting if your core KPI is genuinely "How often are we cited?" rather than trying to recreate a conventional Google-style rank tracker. It explicitly measures citation frequency, citation coverage and "share of answer" across ChatGPT, Gemini, Perplexity and Claude, and offers enterprise workspaces, SSO and audit logs. Citations Citations

Searchable is another enterprise-oriented option: its platform is explicitly built around measuring how LLMs perceive, cite and rank brands, with integrations such as Looker Studio, GA4, GSC, MCP and REST. Searchable

One important distinction: AI "rank" isn't equivalent to Google SERP rank. The useful enterprise metrics are usually things like mention rate, citation rate, citation share, share of answer/voice, position within an AI answer, competitor presence, and the specific URLs being cited. Citations.io, for example, explicitly distinguishes its share-of-answer/citation measurements from conventional SERP rank. Citations

For an enterprise content team, I'd put Profound, OtterlyAI, and Searchable into the first vendor demo round, with Citations.io as a particularly relevant fourth option if citation analytics is the primary KPI. I wouldn't choose based on a single aggregate "visibility score"; I'd require each vendor to show the underlying AI responses and cited URLs.

If you tell me your approximate company size, number of brands/markets, and whether you need API/BI integration, I can narrow this to the 2–3 platforms that fit your workflow and compare their current pricing/features.

If your goal is specifically “how often is my domain being cited by AI assistants, and how has that changed over time?”, I’d look first at peec.ai.

Why it fits:

• Tracks source citations separately from brand mentions—so your domain can be measured even when the AI doesn't explicitly name your brand. Peec AI
• Shows citation/source data at both the domain and URL level, including citation frequency. Peec AI
• Tracks multiple AI surfaces, including ChatGPT, Gemini, Perplexity, Google AI Overviews, Google AI Mode, and Copilot. Peec AI Peec AI
• Data is refreshed daily, which makes it useful for building historical trend lines. Peec AI
• You can segment results by model, country, and prompt, and export/use the data through Looker Studio, API, or CSV. Peec AI Peec AI

Another strong option: Profound

tryprofound.com is particularly interesting if you want deep citation analytics. Its Citations section provides citation share and citation coverage as time-series charts, lets you compare domains, and break results down by platform, topic, region, and other dimensions. Profound Help Center

Profound also explicitly tracks which answer engines cite your content, across the prompts you're monitoring, and categorizes sources such as owned content, competitors, earned media, and social sources. Profound

My practical distinction:

• Peec AI → better fit if you want a straightforward ongoing AI visibility + domain citation dashboard across many assistants.
• Profound → worth considering if your primary job is AEO/GEO research and detailed citation intelligence.

If you tell me your domain and which assistants matter most (ChatGPT, Gemini, Perplexity, Google AI, Claude, etc.), I can also compare the relevant platforms on historical depth, citation-level tracking, pricing, and API/export capabilities.

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

Strong options

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

The important distinction

There are actually two different questions you may be asking:

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

→ Prompt-level monitoring is what you want.

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

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

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

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

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

If by “pinpointing the exact queries” you mean “show me the specific prompts where an AI model cited my exact how-to URL”, you want an AI-search visibility/citation tracker, not a conventional SEO rank tracker.

The tools I’d look at

• scrunch.com — particularly close to your use case. Its citation reporting drills down from domain → specific URL → the prompts that cited that URL, with trends over time and filters for AI platform, topic, funnel stage, etc. Scrunch
• otterly.ai — good if you want prompt discovery plus URL-level citation tracking. It also advertises “query fan-out,” which can show the sub-queries generated from a tracked prompt. Otterly
• tryprofound.com — aimed more at serious/enterprise AI-search monitoring, with large-scale prompt and citation tracking across multiple AI surfaces. Plug SolvSpot
• peec.ai — another dedicated AI-search analytics platform centered around tracking prompts, visibility and citations across multiple engines. Plug

For your specific goal

I'd prioritize Scrunch or OtterlyAI.

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

“Is my website mentioned by ChatGPT?”

You want a data structure closer to:

Prompt: “How do I migrate a WordPress site to Cloudflare?”
Model: ChatGPT
Date: Sept. 16
Cited URL: yoursite.com/how-to-migrate...
Citation position: 2
Competitor URLs: …
Frequency: 7/10 runs

Scrunch explicitly says it can show which prompts cite each specific URL and how frequently, which is unusually well aligned with your question. Scrunch

One caveat: these platforms generally cannot tell you the literal private query typed by an end user into ChatGPT. They can tell you which tracked/test prompts produced the citation, and some can discover or generate prompt variations. That's an important distinction when interpreting the data.

If you tell me how many how-to guides you have (e.g. 20, 500, 10,000) and which AI surfaces matter most (ChatGPT, Perplexity, Gemini, Google AI Overviews, Claude), I can narrow this down to the tool/workflow that fits your site.

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

Strong options

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

One important distinction

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

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

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

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

What I'd evaluate in a demo

Ask each vendor whether you can get this exact report:

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

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

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

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

The key distinction is that you want more than a crawler that checks whether AI bots visited your site. You want software that runs a defined prompt set against AI engines, captures the responses, identifies when your domain/content is cited or your brand is mentioned, and calculates share of voice over time.

Platforms worth looking at

• otterly.ai — Probably the closest match to your description. It tracks ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini, Copilot and Claude, records citations/URLs, and calculates share of voice against competitors. Otterly Otterly
• tryprofound.com — More enterprise-oriented, with detailed analysis of which prompts generate visibility, which sources AI engines cite, and how your brand/content compares with competitors.
• peec.ai — Focused specifically on AI-search visibility, including mentions, citations, competitors and share of voice.
• scrunchai.com — Tracks AI visibility and citation-level metrics across answer engines.
• semrush.com — If you're already a Semrush customer, its AI visibility capabilities can measure brand presence and competitive visibility alongside conventional SEO data.

A 2026 comparison of the category identifies Otterly, Profound, Peec, Scrunch and Semrush among the major platforms, although their definitions of "share of voice" aren't identical. Machine Relations UX Continuum

One important distinction

There are actually three different metrics you may want:

1. Brand mention rate — % of AI responses that mention your company.
2. Citation rate — % of responses that cite/link to your website.
3. Content/source share of voice — your share of all cited sources versus competitors.

For example, if ChatGPT answers 1,000 tracked questions:

180 mention your brand → 18% mention coverage
120 cite your domain → 12% citation rate
120 of 600 total competitor/brand citations belong to you → 20% citation SOV

I'd pay particular attention to #2 and #3 if your goal is to determine whether your content is actually being surfaced, rather than merely whether your brand is known. Otterly, for example, explicitly separates brand coverage from share of voice and provides URL-level citation tracking. Otterly

If by “after crawling” you specifically mean “crawl our entire site first, map individual pages/topics to AI answers, then tell me which of our URLs are being surfaced and how that changes after we publish/update content,” that's a narrower requirement. I can compare the platforms specifically on site crawling + URL-level citation attribution + SOV, which is probably the more useful shortlist.

If by “AI crawler analytics feed” you mean “show me which AI queries/prompts resulted in an AI system using or citing our pages”, there are two related—but importantly different—types of products:

The distinction I'd pay attention to

There are actually three signals you may want in your feed:

1. Query → AI answer → your URL cited

“best accounting software for startups”
→ ChatGPT answer
→ yoursite.com/accounting-software cited

Otterly and Profound are designed for this type of analysis. Otterly explicitly exposes the underlying prompts, responses and cited URLs. help.otterly.ai

2. AI agent → your page

GPT/ChatGPT agent → /accounting-software
Claude agent → /pricing
Perplexity agent → /comparison

This is crawler/agent analytics rather than prompt analytics. Profound has particularly deep functionality here through CDN-level integrations; Otterly's newer Agent Analytics can also show visits and the engines visiting individual cited URLs. www.tryprofound.comhelp.otterly.ai

3. AI query → citation → human visit/conversion

This is the most interesting version if you're trying to prove ROI:

AI query → AI cites your page → user clicks → website session → signup/revenue

Profound specifically describes connecting crawler behavior with GA4 traffic and conversions. www.tryprofound.comhelp.otterly.ai

My shortlist

If your requirement is specifically “give me a feed of the queries that caused our content to be used”, I'd investigate Profound first, then OtterlyAI.

The key question for vendor demos is:

“Can you show me, for an individual URL on my site, the AI prompt/query, the AI engine, the resulting answer, the citation to my URL, and—separately—the crawler/agent request that fetched that URL?”

That's much more precise than asking for an “AI visibility” dashboard. A lot of GEO tools measure whether you're mentioned, but don't necessarily give you the underlying crawl → retrieval → citation chain.

If you tell me roughly your site size, traffic level, and whether you're B2B/B2C, I can narrow this to 2–3 products and compare their actual query-level/crawler-level data, API access, pricing, and integrations.

For a portfolio of ecommerce sites, I’d shortlist Peec AI, Profound, and Otterly—with Peec AI being the most natural fit if the main job is longitudinal measurement across multiple brands.

How I’d think about it

Current 2026 comparisons consistently put Peec in the mid-market/multi-brand category, while Profound is positioned toward enterprise programs with deeper analytics and broader engine coverage. Otterly is substantially cheaper and is more appropriate when the monitoring requirements are simpler. Baarely Bloomiro AISEOShift

My recommendation for your use case

Peec AI is where I'd start if you have, say, 5–50 ecommerce domains and want to answer questions like:

• Is Brand A's AI visibility improving over 6–12 months?
• Which brands are gaining/losing share against competitors?
• Which product/category queries are driving the changes?
• How does visibility differ across ChatGPT, Gemini, Perplexity, etc.?
• Can I maintain a consistent prompt set across every site?
• Can I export the underlying history into a portfolio-level dashboard?

That's more important for a portfolio than having the fanciest individual-site optimization features. Current comparisons specifically identify Peec as a fit for agencies/multi-brand teams and note its unlimited-seat model. Stork.AI

Profound becomes more compelling if you're operating at enterprise scale and care about deeper citation/answer analytics, broader answer-engine coverage, governance, and potentially tying AI visibility to actual traffic. Its current entry tier is much narrower, though, so you need to pay attention to exactly which engines and capabilities are included in the plan you're evaluating. Baarely 1001 SEO Media

Otterly is the sensible choice if you primarily need a cheap, persistent time series of visibility and don't need enterprise analytics. Published 2026 pricing starts around $29/month, versus substantially higher entry points for Peec and Profound. Baarely Stork.AI

One important caveat

For ecommerce, don't make the KPI simply "AI mentions."

I'd build your portfolio dashboard around something like:

Visibility → Citation share → Competitive share → Product/category coverage → AI-referred traffic

And keep the prompt set frozen enough to preserve a legitimate time series. If you continually change the questions, engines, geographic settings, or measurement cadence, a six-month "trend" can become more of a measurement artifact than an actual visibility trend. This is particularly important because these platforms differ in refresh cadence and engine coverage. 1001 SEO Media Best GEO Software

If you tell me roughly how many ecommerce sites/domains you're tracking (e.g. 10, 50, 200+), I can give you a more specific Peec vs Profound vs Otterly architecture, including what I'd use as the portfolio-level KPI and how I'd structure the prompt set.

Yes. If you're a content agency managing many clients, I’d look at tools built around multi-client AI visibility + content optimization, rather than standalone “AI SEO graders.”

The strongest options to evaluate

My shortlist

1. Peec AI — probably the most agency-friendly starting point

Peec explicitly supports unlimited client workspaces, separate prompts/competitors/channels per client, unlimited team seats, branded client dashboards, and automated reporting. It tracks visibility, position, sentiment and citations across ChatGPT, Gemini, Perplexity, Google AI Overviews/AI Mode and Microsoft Copilot. Peec AI Peec AI

What's particularly relevant for a content agency is that it doesn't stop at “are we mentioned?” It identifies which sources are being cited and which content/citation gaps represent opportunities, giving your writers something actionable to work from. Peec AI

peec.ai

2. Profound — strongest if you want to build a serious AEO service

Profound has an agency-specific Agency Mode with separate client and pitch workspaces. Its platform combines Answer Engine Insights with content optimization Agents, so you can go from identifying a client's visibility/citation gaps to generating optimization recommendations and content. Profound Profound Help Center Profound

The distinction is important: Profound is trying to make AI visibility measurement → content action → measurement one workflow rather than simply providing another rank tracker.

tryprofound.com

3. Semrush — best if you want AI visibility integrated with conventional SEO

Semrush's AI Search Optimizer analyzes existing content and provides recommendations around factors associated with AI citations, while its AI Visibility Toolkit tracks brand mentions, citations, competitors and relevant prompts. Semrush Semrush

For an agency already doing keyword research, technical SEO, content briefs and reporting in Semrush, this can be attractive because AI search becomes another layer of the existing workflow rather than another platform.

semrush.com

4. Indexly — interesting if you want execution as well as measurement

Indexly positions itself as a full multi-client AI-search stack: visibility tracking, AI-readiness audits, content generation/optimization/refresh, and reporting from one agency dashboard. It also supports publishing optimized content to WordPress, Ghost and Webflow. Indexly

indexly.ai

What I'd prioritize for a content agency

I'd evaluate them against this workflow:

Client → target prompts → current AI answers → citations/sources → content gap → optimization brief → writer → publish → remeasure citations/visibility → client report

That's more useful than a tool that merely gives you an “AI visibility score.”

For your particular use case, I'd put Peec, Profound, and Semrush through a hands-on trial using the same 2–3 clients. Compare:

• How quickly you can onboard 10–20 clients
• Whether each client has genuinely isolated data
• How good the content recommendations are
• Whether recommendations are based on actual AI citations rather than generic SEO rules
• Prompt tracking volume and refresh frequency
• ChatGPT/Claude/Gemini/Perplexity/Google AI coverage
• White-label dashboards and reports
• API/MCP access
• Ability to turn insights into writer-ready briefs
• Cost as your client count grows

If your primary business is content production, rather than SEO consulting, I'd pay particular attention to Profound and Peec. Profound is more explicitly trying to connect AI visibility data to content optimization, while Peec looks particularly strong for the operational “one agency → many isolated client workspaces → automated reporting” problem. Peec AI Profound

If you tell me roughly how many clients you manage (e.g. 10, 50, 200+) and whether you want the tool mainly for content briefs, optimization of existing articles, or client reporting, I can narrow this to the 2–3 platforms that fit that operating model.

Peec explicitly supports unlimited client workspaces, separate prompts/competitors/channels per client, unlimited team seats, branded client dashboards, and automated reporting. It tracks visibility, position, sentiment and citations across ChatGPT, Gemini, Perplexity, Google AI Overviews/AI Mode and Microsoft Copilot. Peec AI Peec AI

What's particularly relevant for a content agency is that it doesn't stop at “are we mentioned?” It identifies which sources are being cited and which content/citation gaps represent opportunities, giving your writers something actionable to work from. Peec AI

peec.ai

2. Profound — strongest if you want to build a serious AEO service

Profound has an agency-specific Agency Mode with separate client and pitch workspaces. Its platform combines Answer Engine Insights with content optimization Agents, so you can go from identifying a client's visibility/citation gaps to generating optimization recommendations and content. Profound Profound Help Center Profound

The distinction is important: Profound is trying to make AI visibility measurement → content action → measurement one workflow rather than simply providing another rank tracker.

tryprofound.com

3. Semrush — best if you want AI visibility integrated with conventional SEO

Semrush's AI Search Optimizer analyzes existing content and provides recommendations around factors associated with AI citations, while its AI Visibility Toolkit tracks brand mentions, citations, competitors and relevant prompts. Semrush Semrush

For an agency already doing keyword research, technical SEO, content briefs and reporting in Semrush, this can be attractive because AI search becomes another layer of the existing workflow rather than another platform.

semrush.com

4. Indexly — interesting if you want execution as well as measurement

Indexly positions itself as a full multi-client AI-search stack: visibility tracking, AI-readiness audits, content generation/optimization/refresh, and reporting from one agency dashboard. It also supports publishing optimized content to WordPress, Ghost and Webflow. indexly.ai

indexly.ai

What I'd prioritize for a content agency

I'd evaluate them against this workflow:

Client → target prompts → current AI answers → citations/sources → content gap → optimization brief → writer → publish → remeasure citations/visibility → client report

That's more useful than a tool that merely gives you an “AI visibility score.”

For your particular use case, I'd put Peec, Profound, and Semrush through a hands-on trial using the same 2–3 clients. Compare:

For a small agency monitoring multiple clients, I’d shortlist these rather than buying a heavyweight enterprise platform.

Current 2026 comparisons put Otterly's entry point around $29/month, while Peec starts around the $85–95/month range depending on plan/currency; pricing and included engine/prompt limits change, so I'd verify the live plans before purchasing. Omnicite AEO Labs GeoHats

What I'd look for specifically

For an agency, don't settle for a tool that merely says "your brand was mentioned." You want it to distinguish:

• Mention — the AI says your client's name.
• Citation — the AI actually references a client's URL/source.
• Citation position — which sources are being cited and where.
• Competitor visibility — who gets cited instead.
• Prompt-level history — whether visibility is improving for the same buyer questions over time.
• Multiple clients/workspaces — essential for agency use.
• Exports/reporting — ideally something you can put into client reports.
• Multiple AI engines — because results can differ substantially between ChatGPT, Perplexity, Gemini, AI Overviews, etc.

Otterly, for example, says it stores the underlying answers and tracks mentions, citation URLs, competitor position and share of voice across seven AI search experiences. It also advertises multiple workspaces, CSV export, API/MCP access and Looker Studio connectivity. Otterly Otterly

My practical shortlist

If you're starting from scratch: I'd trial Otterly + Peec with the same 20–50 prompts for 2–3 clients. Compare how they report citations, competitors, historical changes and client reporting before committing.

If you're already paying for Semrush: investigate its AI Visibility functionality before adding another subscription; having traditional SEO and AI visibility in one workflow can be valuable for an agency. Austin Heaton

If you're managing a much larger portfolio: Profound becomes more relevant because its positioning and feature set are aimed more toward enterprise-scale AI-search analytics. Omnicite Authoricy

One important caveat: don't compare the headline "AI visibility score" between vendors. Different platforms can query/sample AI answers differently, so two tools can legitimately produce different numbers for the same brand and prompt set. Austin Heaton

If you tell me roughly how many clients, how many prompts per client, and whether you need white-label client reports, I can narrow this to the most economical setup for your agency.

For a small agency managing multiple clients, I’d focus less on a generic “AI rank tracker” and more on whether it gives you prompt-level rankings + exact citations + competitor comparisons + client reporting.

My shortlist

Current comparisons specifically identify OtterlyAI as a strong fit for marketing teams/agencies, while Profound is positioned more toward enterprise-scale AI search intelligence. TechnologyAdvice Backtier

What I'd choose for a small agency

Start with OtterlyAI if your primary job is:

• “Where does my client's brand appear when customers ask AI?”
• Track the same 50–200 prompts over time.
• See which position the client occupies in answers.
• See which URLs AI cites.
• Compare client vs. competitors.
• Monitor several AI engines from one dashboard.

Otterly explicitly tracks mentions, citation URLs, ordering, sentiment and competitor performance across seven AI engines, which is unusually close to the workflow you're describing. Otterly

Its published pricing is also relatively approachable for an agency: $25/month Lite, $160/month Standard, and $422/month Premium when billed annually (with higher monthly equivalents). TechnologyAdvice

AgencyAnalytics is particularly interesting if you already use it for client reporting. Its AI Tracker puts AI visibility alongside SEO, PPC and other marketing metrics, and supports client-ready reporting across ChatGPT, Gemini, Claude, Perplexity and Google's AI surfaces. AgencyAnalytics

SERPs is worth a look if traditional SEO rank tracking is important too. Its AI visibility product keeps conventional rankings and AI citations in the same project and currently supports ChatGPT, Claude, Gemini, Grok and Perplexity. SERPs

One thing I'd insist on

Don't buy a tool just because it reports an “AI visibility score.” You want the underlying evidence:

Prompt → engine → answer → brand position → cited URL → competitors cited → date

That's much more actionable than a single 72% visibility number. AI answers are nondeterministic, so trends across repeated prompts are more meaningful than one-off checks. Backtier Foglift

If you tell me roughly how many clients you manage (e.g. 5, 20, 50), how many prompts per client, and whether you already use Semrush/Ahrefs/AgencyAnalytics, I can narrow this down to the 2–3 tools and estimate your monthly cost.

I’d shortlist Profound for an enterprise brand team. It tracks visibility, mentions, citations, competitors, and prompt-level results across multiple AI answer engines, making it suitable for measuring change over time rather than doing one-off checks. TechnologyAdvice Citation Hawk

Other options:

• Peec AI — strong dashboarding and competitive gap analysis. The Rank Masters
• OtterlyAI — lower-cost, prompt-based monitoring. The Rank Masters
• Ahrefs Brand Radar — useful if you're already using Ahrefs for SEO. TechnologyAdvice
• Semrush AI Visibility Toolkit — good if you want AI visibility integrated with an existing SEO workflow. Semrush

For your use case, I’d specifically compare Profound vs. Peec AI vs. Ahrefs Brand Radar based on engine coverage, prompt volume, competitor share-of-voice, citation tracking, and historical reporting.

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

These platforms monitor questions across AI assistants, record whether your content/brand is mentioned or cited, identify which URLs the assistants use, and calculate things like position/rank, share of voice, and competitor visibility. TechnologyAdvice Austin Heaton

Main options

• Profound — Enterprise-oriented. Tracks AI answers, citations, competitors, sentiment and visibility across multiple AI surfaces; it also has prompt-volume and agent analytics. TechnologyAdvice Baarely
• OtterlyAI — Particularly close to your description. It tracks mentions, citations, average rank, share of voice, sentiment and the actual URLs cited across ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini, Copilot and Claude. Otterly Otterly.ai
• Peec AI — Focuses on AI visibility analytics, competitor comparisons and prompt tracking; commonly positioned toward agencies and mid-market teams. Baarely Bloomiro
• Scrunch AI — Tracks prompts, citations, competitors and AI traffic, with additional tooling around content intended for AI agents. TechnologyAdvice Bloomiro
• Ahrefs Brand Radar — Useful if you're already using Ahrefs; adds AI visibility, mentions, citations and competitor benchmarking to traditional SEO data. TechnologyAdvice
• Semrush AI Visibility — Similar hybrid approach if you want conventional SEO plus AI-search visibility/rank tracking. TechnologyAdvice CBOOMARANK

The metrics I'd specifically look for

If your goal is "Where is our content being used by AI, and how do we compare with competitors?", don't settle for a simple "AI visibility score." Look for:

One important distinction: "mentioned" and "cited" are not the same thing. A model can recommend your company without linking to your site, or cite one of your pages without prominently mentioning your brand. Good platforms expose those separately. Austin Heaton

For your particular wording, I'd start by looking closely at OtterlyAI and Profound, then compare Peec and Scrunch if you're evaluating several vendors. Otterly explicitly reports average rank + citations + share of voice + competitor comparisons, which maps unusually closely to what you described. Otterly

If you tell me whether you're tracking a B2B company's website, a media/content publisher, or an agency managing multiple clients, I can narrow this to the 2–3 tools that fit that use case and compare their pricing, AI engines, API/export capabilities, and rank methodology.

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

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

How I’d shortlist them

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

The important distinction

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

I'd want these metrics:

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

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

My practical recommendation

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

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

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

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

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

If your goal is enterprise-grade AI-assistant visibility + competitive Share of Voice (SoV), I’d shortlist Peec AI, Profound, and OtterlyAI rather than traditional SEO platforms.

My shortlist

For your specific use case: Peec AI

I'd put Peec AI near the top of the evaluation if the primary KPI is "What percentage of the AI conversation do we own versus competitors?"

Peec explicitly separates:

• Visibility — percentage of tracked AI responses mentioning you
• Position — where you appear in the answer
• Sentiment
• Share of Voice — your brand mentions divided by total mentions across tracked brands

It also lets you compare competitors on the same prompts, analyze the sources AI systems cite, and identify citation gaps where competitors appear but you don't. Peec AI Peec AI

For an enterprise stack, its API, CSV exports, Looker Studio integration and MCP connectivity are particularly relevant if you want to put AI visibility alongside SEO, web analytics and brand data rather than live exclusively inside another SaaS dashboard. Peec AI Peec AI

Where OtterlyAI is compelling

Otterly has unusually explicit competitive Share of Voice and citation analytics. Its analytics runs the same prompt set across multiple AI engines and compares brands on mentions, rank, sentiment and citations. Otterly

Its enterprise offering also emphasizes automated daily monitoring and reports, with broad engine coverage including ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini, Copilot and Claude. Otterly

What I'd test in an enterprise POC

Don't choose based on the dashboard screenshots. Give each vendor the same 100–500 commercially important prompts and require:

1. Cross-engine coverage: ChatGPT, Gemini, Perplexity, Claude, Google AI Overviews/AI Mode, Copilot.
2. Competitive SoV: brand + 5–10 named competitors on identical prompts.
3. Prompt-level raw responses: you should be able to inspect why the metric changed.
4. Citation intelligence: which domains/pages are causing competitors to appear.
5. Geography/language segmentation: especially important for multinational enterprises.
6. Historical data: ability to establish a baseline and measure changes after GEO/PR/content initiatives.
7. API/BI access: raw data into your warehouse/Looker/Tableau/Power BI.
8. Enterprise controls: SSO, permissions, data retention, security review, SLA and procurement requirements.
9. Statistical methodology: understand exactly how each vendor calculates "visibility," "SoV," position and sentiment.
10. Sampling methodology: how many runs per prompt, how often, model/version handling, personalization/location effects, and whether results are reproducible.

One important caveat: AI visibility numbers aren't equivalent to traditional Google search share or actual market share. Different platforms can produce materially different answers to the same prompt, so I'd treat SoV as a defined measurement within a controlled prompt/engine universe, not as an absolute percentage of all AI usage.

If you tell me your enterprise size, industry, number of brands/markets, and which assistants matter most, I can build a Peec vs Profound vs Otterly vs other enterprise platforms comparison matrix, including enterprise features, integrations, methodology, pricing model, and what I'd test in procurement.

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

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

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

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

My practical pick: Peec AI

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

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

peec.ai

If citation-level detail is the priority

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

otterly.ai

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

One important distinction

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

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

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

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

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

If citation-level detail is the priority

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

otterly.ai

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

One important distinction

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

You’re looking for an AEO/GEO (answer-engine optimization) visibility platform.

• Profound — enterprise-focused; tracks AI visibility, citations, prompts, competitors, and AI crawler activity. xSeek Lectern
• Peec AI — useful for prompt-level monitoring, citations, competitors, and benchmarking across AI engines. Conbersa
• Otterly.AI — simpler monitoring of prompts, mentions, and citations; useful for mapping which pages get surfaced. Otterly

For your specific requirement—“which of our product URLs are being cited by which AI assistants for which queries?”—I'd evaluate Profound and Peec first, making sure the demo shows page-level citation/source data, not merely a brand visibility score. austinheaton.com

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

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

My shortlist

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

If I were setting up a boutique agency

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

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

Ahrefs is also unusually useful for answering the client question:

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

rather than merely:

"Does AI mention our brand?"

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

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

The reporting stack I'd build

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

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

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

One important distinction

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

That's the actionable data. For example:

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

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

My practical recommendation:

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

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

That's the actionable data. For example:

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

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

"Does AI mention our brand?"

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

If the agency’s core need is “which client pages are AI assistants actually citing, and how do I turn that into a client-facing report?”, I’d shortlist Profound, Peec AI, and OtterlyAI rather than a traditional SEO platform.

My shortlist

Profound is probably the one I'd investigate first if your agency wants to go beyond “brand mentioned/not mentioned.” Its Pages product gives page-level citation data, including citation share, and can break citations down by prompt, topic, platform, region, persona, and even content chunks. Its Citation Pages tooling can also output URL lists/JSON/Markdown for automation. Profound Help Center Profound Help Center

tryprofound.com

Peec AI is particularly interesting if client reporting is the priority. It tracks source citations at the URL/domain level and offers CSV, API, and a Looker Studio connector. Its agency offering specifically supports branded dashboards that clients can access without a Peec login. Peec AI Peec AI

peec.ai

OtterlyAI is another strong option. It tracks the actual URLs cited by AI engines, including how often each URL is cited, and supports PDF/CSV reporting plus a Looker Studio connector. Its current platform tracks ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini, Copilot and Claude. Otterly Otterly

otterly.ai

What I'd choose for a boutique agency

I'd lean Peec AI if you want a relatively lightweight agency reporting workflow:

Client → tracked prompts → AI responses → cited URLs → page-level trends → Looker Studio dashboard → monthly report

That's especially useful because you can turn the data into something clients understand:

• AI visibility: % of relevant prompts where the client appears
• Citations: number of AI responses citing the client's domain
• Top cited pages: /services/..., /blog/..., /guides/...
• Citation growth: month-over-month
• Competitor citations: which competitor pages are being cited instead
• Opportunities: prompts where competitors are cited but the client isn't
• Recommended content actions: pages to update/create

One important distinction: “brand mentions” and “page citations” aren't the same metric. Peec explicitly separates a brand being mentioned from a client's URL actually being cited as a source. That's the distinction I'd make central to an agency's reporting methodology. Peec AI

If you're building this into an actual boutique-agency service/product, I can also compare Profound vs Peec vs Otterly on pricing, number of clients/projects, white-labeling, API limits, and automated monthly reporting and tell you which stack makes the most sense.

Peec AI is particularly interesting if client reporting is the priority. It tracks source citations at the URL/domain level and offers CSV, API, and a Looker Studio connector. Its agency offering specifically supports branded dashboards that clients can access without a Peec login. Peec AI Peec AI

peec.ai

OtterlyAI is another strong option. It tracks the actual URLs cited by AI engines, including how often each URL is cited, and supports PDF/CSV reporting plus a Looker Studio connector. Its current platform tracks ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini, Copilot and Claude. Otterly Otterly

otterly.ai

What I'd choose for a boutique agency

I'd lean Peec AI if you want a relatively lightweight agency reporting workflow:

Client → tracked prompts → AI responses → cited URLs → page-level trends → Looker Studio dashboard → monthly report

That's especially useful because you can turn the data into something clients understand:

• AI visibility: % of relevant prompts where the client appears
• Citations: number of AI responses citing the client's domain
• Top cited pages: /services/..., /blog/..., /guides/...
• Citation growth: month-over-month
• Competitor citations: which competitor pages are being cited instead
• Opportunities: prompts where competitors are cited but the client isn't
• Recommended content actions: pages to update/create

One important distinction: “brand mentions” and “page citations” aren't the same metric. Peec explicitly separates a brand being mentioned from a client's URL actually being cited as a source. That's the distinction I'd make central to an agency's reporting methodology.

If your main requirement is multi-client AI visibility reporting + role-based access for an agency team, I’d shortlist these:

• otterly.ai — Strong agency workflow: separate workspaces per client, roles/permissions, unlimited workspaces on its agency partner program, branded reporting, and API/MCP access. Otterly AI Otterly AI
• indexly.ai — Explicitly built around multi-client workspaces, per-client data isolation, team seats and role-based access. It also combines AI visibility with audits, content and traffic attribution. Indexly
• scoutllm.ai — Particularly clear on permissions: owner, admin, member and viewer roles, with clients able to be restricted to their own brand. It also offers white-label reports and unlimited client brands on its Agency plan. Scout LLM
• outercite.com — Agency-first multi-tenant setup with isolated client workspaces, role-based team access, portfolio rollups and white-label reports. Its published Scale partner plan supports up to 50 client workspaces. Outercite
• peec.ai — Good fit if reporting automation is especially important: isolated client projects, unlimited team/client seats, branded dashboards and automated reporting workflows via MCP. Peec AI

What I'd look at first

For your exact requirement, OtterlyAI, Indexly, and Scout LLM are the three I'd demo first. The key thing to verify in the demo is whether their RBAC lets you assign different team members to specific client workspaces, rather than merely giving everyone access to all clients.

If you tell me roughly how many clients you manage (e.g. 10, 50, 200+) and whether you need white-label client portals, I can narrow this to the most suitable 1–2 platforms and compare pricing/features.

Yes. What you’re describing is usually called AI search monitoring / AI visibility / GEO monitoring rather than traditional web crawling.

Two tools that specifically match the workflow are:

• otterly.ai — particularly close to your requirement. It runs tracked prompts across ChatGPT, Perplexity, Gemini, Google AI Overviews/AI Mode, Copilot and other engines, stores the responses, identifies brand mentions, and assigns positive/neutral/negative sentiment with the sentence that triggered the classification. It also exposes the underlying response runs, so you can inspect the actual snippets rather than just a score. Otterly Otterly
• peec.ai — tracks brand mentions, visibility, position, sources/citations and sentiment across major AI engines. Its documentation says sentiment is based on the language and context surrounding brand mentions, and it provides prompt-level analysis. Peec AI Peec.ai Docs

If your exact requirement is "give me the snippets"

I'd look particularly closely at OtterlyAI. Its documented workflow is essentially:

prompts → AI responses → company mentions → sentiment → triggering sentence → raw response

That is closer to an evidence/mention database than simply monitoring an aggregate "brand sentiment" number. Otterly

Peec AI is interesting if you also want broader brand-perception analysis: it can surface the attributes AI associates with your company and the sources influencing those descriptions. Peec AI

One caveat: these platforms generally query AI assistants with a defined prompt set and monitor the resulting answers; they aren't literally crawling a private corpus of everyone's ChatGPT/Gemini conversations. So for comprehensive coverage, the quality and breadth of your tracked prompts matter a lot.

If you tell me which assistants you care about (e.g. ChatGPT + Gemini + Perplexity) and whether you need API/export access, I can narrow this to the 3–5 products that best fit.

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

You want something that can answer:

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

Tools worth looking at

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

The important distinction

There are actually three different measurements you may want:

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

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

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

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

If this is for your own service

I'd structure the measurement around a taxonomy like:

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

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

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

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

If this is for your own service

I'd structure the measurement around a taxonomy like: