Neutral mentions of Peec AI

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

• $29/month for its Lite plan, with 15 prompts tracked daily. Otterly
• It monitors ChatGPT, Google AI Overviews, Perplexity, and Microsoft Copilot, so you can see whether your brand/products appear in answers and what gets cited. Otterly Otterly
• There’s a free trial with no credit card required, which makes it relatively low-risk to test. Otterly

One important ecommerce caveat

If your main question is specifically “Does AI recommend my individual product pages/SKUs?”, rather than just “Does AI mention my brand?”, peec.ai has more ecommerce-specific functionality. It supports product-catalog uploads, SKU-level tracking, shopping-query analysis, and visibility of the domains/URLs AI retrieves when recommending products. Peec AI

The tradeoff is price: Peec's Starter plan is currently listed at $95/month for 50 prompts and 3 models, so it's substantially more expensive. Peec AI

My budget approach: start with Otterly's 15 prompts and use them for your highest-value product/category queries—for example, “best _ for ,” “ alternatives,” and “where can I buy _?” If you eventually need SKU-level shopping visibility, that's when I'd consider moving to Peec.

If you tell me roughly how many products you sell and whether you're on Shopify, I can suggest a 15-prompt tracking setup that gets you the most useful signal for $29/month.

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

• “When shoppers ask AI for products like mine, does my product appear?”
• “Which competitors are being recommended instead?”
• “What product pages or content should I improve?”

My shortlist:

My recommendation for a small store:

1. Start with Otterly.AI or Rankscale for 1–2 months.
2. Create a list of ~20–50 buyer prompts, such as: - “best [product category] under $100”
3. “alternatives to [competitor product]”
4. “best [product] for [specific use case]”
5. Track: - whether your brand/product is mentioned
6. whether your product page is cited
7. which competitors appear
8. what sources AI systems rely on
9. Spend your optimization effort on: - clearer product descriptions
10. strong FAQs
11. comparison pages (“Product A vs Product B”)
12. structured product data/schema
13. customer reviews and third-party mentions

A free/cheap approach is also viable: keep a spreadsheet of your important prompts and manually test them weekly in ChatGPT, Perplexity, Gemini, and Claude. This is slower, but for a small catalog it can reveal most of the important gaps before paying for software. techradar.com

If you tell me:

• your ecommerce platform (Shopify, WooCommerce, Amazon, custom, etc.)
• number of products/SKUs
• monthly budget (e.g., $0, $25, $100)

…I can narrow it down to one tool.

Yes. For a startup with no developer resources, I’d shortlist these:

• otterly.ai — best budget choice. It starts at $29/month, has a free trial, and monitors brand mentions across ChatGPT, Perplexity, Google AI Overviews, Gemini, Copilot, and Claude. You define prompts such as “best [category] software” and it automatically checks the answers and tracks mentions, rankings, sentiment, and citations. Otterly Otterly
• tryprofound.com — best if you want deeper analysis. Its Starter plan is $99/month billed annually, with 50 tracked prompts and ChatGPT tracking; higher tiers expand to multiple answer engines. It also analyzes sentiment, rankings, citations, and competitors. Profound Profound
• peec.ai — good middle ground. Its Starter plan is $95/month, with 50 prompts across three models and daily tracking. It emphasizes visibility, position, sentiment, share of voice, and which sources influence AI answers. Peec AI Peec AI

What I'd pick

Start with OtterlyAI. At $29/month, it's the closest fit to “I just want to know when AI starts mentioning my brand, without hiring anyone to build this.” G2 Otterly

One important distinction: these tools generally don't listen to every AI conversation happening on the internet. They run a set of representative prompts against AI engines and measure whether/how your brand appears. So you'd want to create prompts covering:

• Your brand/product name
• “[category] alternatives”
• “best [category] tools”
• “X vs [competitor]”
• “[problem your product solves]”
• “[competitor] alternatives”

If you tell me your product/category and roughly what you can spend ($0, <$50, <$100/month), I can narrow it down to the cheapest setup I'd actually use.

For an enterprise needing historical, cross-model brand visibility analytics, I’d shortlist:

• tryprofound.com — strongest fit for enterprise-scale monitoring, broad model coverage, historical prompt/visibility analytics, citations, and competitor benchmarking. Baarely Marqeable
• business.adobe.com — compelling if you already use Adobe; combines AI visibility measurement with optimization and existing analytics workflows. Adobe for Business Experience League
• peec.ai — good analytics-focused alternative for multi-market, multi-engine tracking. Baarely

For your specific requirement, I’d start with Profound and Adobe in an enterprise bake-off. Make vendors demonstrate reproducible historical snapshots, model/version tracking, geographic/language segmentation, citation-level provenance, API/data export, SSO/RBAC, and retention—not just a current “visibility score.”

For a marketplace with thousands of listings, I’d prioritize a tool that treats the SKU/product URL as the unit of measurement, rather than one that only measures brand mentions.

Best fits

• Searchpanel — probably the closest match to your requirement. It explicitly tracks every product/SKU across AI answers, with per-SKU visibility scores, prompts, competitors, citations, and daily monitoring. It says it can scale across an entire catalog and track ChatGPT, Gemini, Perplexity, Copilot, Google AI Overviews, and Amazon Rufus. Searchpanel Searchpanel

searchpanel.ai

• Zumi — another strong option for a large retail/ecommerce catalog. It tracks how AI engines describe, compare, and rank individual SKUs, and can roll those results up into category/segment visibility. It supports up to nine engines depending on engagement. Zumi

zumihq.com

• SixthShop — specifically positioned around product-level AI shopping visibility, including product-page analysis and recommendations across ChatGPT, Gemini, Perplexity, Claude, Rufus, and Google AI Overviews. It may be particularly interesting if you want optimization as well as monitoring. Sixthshop Sixthshop

sixthshop.com

What I'd choose for a marketplace

If you're talking about 10,000–1M+ listings, I'd evaluate them on these dimensions rather than generic "AI visibility":

1. SKU ingestion: API/feed/catalog upload rather than manually entering URLs.
2. Prompt-to-SKU attribution: e.g. "best running shoes under $150" → which of your SKUs were recommended and in what position.
3. Citation/page attribution: which product pages, reviews, marketplaces, or third-party sources caused the recommendation.
4. Historical monitoring: visibility change per SKU over days/weeks.
5. Sampling economics: ability to monitor thousands of SKUs without requiring thousands of individually expensive prompt sets.
6. API/export: critical if you want to put AI visibility into your marketplace's internal merchandising dashboard.
7. Competitor SKU tracking: not just competitor brands.
8. Engine coverage: ChatGPT, Gemini, Perplexity, Google AI Overviews/AI Mode, Claude, and shopping-specific agents.

For your exact use case, I'd put Searchpanel and Zumi at the top of the evaluation list. Traditional platforms such as Profound, Peec, and Otterly are useful for broader AI/brand visibility, but many tools in that category are fundamentally brand/prompt monitors rather than true catalog/SKU monitors. notice.cx conbersa.ai

If you tell me roughly how many SKUs (10K, 100K, 1M+) and which marketplace stack/API you have, I can narrow this down to the most scalable architecture and estimate how you'd monitor the catalog without querying every SKU × every prompt.

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. The category you’re describing is emerging as AI search visibility / GEO (Generative Engine Optimization) / AEO tooling. But there’s an important distinction:

Most products today measure which pages AI assistants actually cite. Far fewer provide a true prediction of which pages are likely to be cited next.

Closest products today

• Profound — probably the closest match. Its Citation Intelligence tracks the pages AI answer engines actually cite, including a ranked Top Citation Pages view. It also has page-level analytics, content scoring, and recommendations for pages to create/update. Profound Profound Help Center

tryprofound.com

• Peec AI — tracks both brand mentions and source visibility, down to the URL level. It identifies which third-party sites are most frequently cited for a prompt/category and provides prioritized actions around owned pages, editorial coverage, reference sites, and communities. Peec Peec

peec.ai

• Scrunch AI — positioned around AI visibility and tracking how brands appear in answer engines. It's in the same general competitive set, although the differentiator isn't specifically a predictive "this URL has an 82% probability of being cited" model. Bloomiro
• Otterly.ai — another AI-search monitoring product that tracks citations/mentions across answer engines, but is more oriented toward monitoring than predictive page selection. Bloomiro

The gap I see

If your proposed tool is:

"Give me 10,000 pages and predict which ones an AI assistant is most likely to retrieve/cite for a given question."

That's a more interesting—and somewhat different—product.

The existing platforms largely work backwards:

Prompt → run AI answer → observe citations → rank cited URLs

What you're describing sounds more like:

Query + corpus of pages → predict retrieval/citation probability → rank pages before the AI answer happens

For example:

And potentially:

"Why does this page have a 87% probability?"

→ semantic relevance
→ entity coverage
→ factual density
→ freshness
→ authority
→ structure/extractability
→ historical citation behavior
→ similarity to pages already retrieved by ChatGPT/Perplexity/Gemini
→ corroboration by other sources
→ query-specific passage quality.

The really interesting version

I'd actually frame the product as "AI Citation Prediction", rather than another AI visibility tracker.

The competitive distinction becomes:

Profound / Peec:
"Here are the sources AI is citing."

Your product:
"Before you publish or optimize, here are the pages AI is most likely to cite—and here's what would increase their probability."

That could be particularly valuable for large publishers, ecommerce catalogs, SaaS knowledge bases, and agencies, where the problem isn't knowing that 20 pages were cited; it's deciding which of 100,000+ pages deserve optimization investment.

Profound is getting closer to this with its page-level scoring and recommendations, so I'd consider it the benchmark to beat. Profound Help Center

If you're evaluating this as a product opportunity, I can also map 10–15 competitors by exactly what they predict/measure, their APIs/data sources, pricing, and the whitespace for a genuinely differentiated product.

If your real requirement is “find incorrect AI attributions, identify the exact source/citation, and route the issue to the content owner who can fix it,” I’d narrow the field to Profound and Otterly.AI, with Profound as my first choice for an enterprise content operation.

My pick: Profound

tryprofound.com is the one I'd evaluate first if you're building a serious AI-answer attribution QA program. It is positioned around source-level citation intelligence rather than merely measuring whether your brand was mentioned, and current comparisons consistently put it toward the enterprise end of the category. Pressfit.ai SEO Counselors

The workflow I'd want is:

AI prompt → generated answer → cited URL → expected source → attribution error → content owner → correction → re-test

The critical distinction is that “brand mentioned” isn't the same as “correctly cited.” A useful system needs to preserve the underlying answer and the exact URLs cited, so your team can determine things like:

• AI says “Company X's policy is…” but cites the wrong article.
• AI attributes a statistic to your company that actually came from a competitor.
• AI combines facts from two of your pages and attributes them to one.
• An old page is cited even though your newer canonical page contains the correct information.
• Your content is cited but the model misrepresents what the page says.
• A competitor's page is being cited for information that your authoritative page actually owns.

Otterly is particularly interesting if you want a less expensive starting point: its current product says it stores generated answers, tracks every cited URL, and lets you inspect citations by prompt, engine, competitor, etc. It also exposes API/MCP and reporting capabilities. Otterly

Where I'd be careful

Most “AI visibility” platforms are fundamentally monitoring systems, not true attribution-QA systems. They can tell you “this URL was cited”, but that doesn't automatically mean “the citation is factually correct.” That's a much harder layer requiring comparison of the AI claim against the source content. Current comparisons make the same distinction: visibility tools measure the problem; they generally don't fix the underlying content. Lyra UX Continuum

So if incorrect attribution is your primary KPI, I'd actually evaluate vendors using a sample of 100–500 real buyer prompts and score each platform on:

1. Claim extraction — can you see what the AI actually claimed?
2. Citation extraction — exact URL cited?
3. Claim ↔ source verification — can you determine whether the source actually supports the claim?
4. Page-level ownership — map the URL to your CMS/content owner.
5. Error classification — wrong source, outdated source, unsupported claim, competitor attribution, etc.
6. Workflow integration — Jira/Linear/Slack/Asana/webhook/API.
7. Re-test history — after the writer fixes the page, can you measure whether the error disappeared?

If #3–#7 are non-negotiable, don't buy based on an “AI visibility score.” Ask vendors to demonstrate the workflow using your own content before signing.

If you tell me roughly how many pages/prompts you have and whether your content team works in Jira, Asana, Linear, or a CMS, I can give you a much more specific 3-tool shortlist and an evaluation scorecard.

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

For a small content agency, I’d shortlist these:

• otterly.ai — probably the best starting point. It tracks how often clients are mentioned across ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini, Claude and Copilot, and captures the URLs/domains cited in the answers. It also has competitor/share-of-voice tracking, reporting, exports and an API. Otterly Otterly
• Peec AI — strong agency/multi-client option. It is frequently positioned as a good fit for agencies and mid-market teams, particularly for citation-level analysis and monitoring multiple brands. Prism News GeoDocs
• Profound — more enterprise-oriented. Stronger if you eventually need sophisticated citation intelligence, large prompt sets and enterprise reporting, but likely more than a small agency needs initially. Bloomiro SEO Counselors

What I'd use for your agency

I'd start with OtterlyAI. The particularly useful distinction is that it doesn't just give you a "client mentioned 37% of the time" score. It can show which pages/sites AI actually cited, how citation frequency changes, competitors being cited instead, and the underlying prompts/answers. Otterly

That lets you turn this into a client-facing monthly metric such as:

AI visibility: 42% → 57%
Share of voice: 18% → 24%
Client mentions: 86/200 prompts
Client citations: 31
Most-cited content: /industry-report, /pricing, /guide
Competitor citations gained: 12
Citation opportunities: 23 prompts where competitors appeared but client didn't

That's considerably more compelling for a content agency than simply reporting "AI traffic."

If you tell me roughly how many clients you manage (e.g. 5, 20, 50+) and your monthly software budget, I can narrow this down to the best 2–3 options and compare multi-client workspaces, white-label reports, citation capture, API access, and pricing.

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 — 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.

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.

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.

Yes. The category you’re looking for is usually called AI visibility, AI search visibility, GEO (Generative Engine Optimization), or AEO (Answer Engine Optimization) software.

These tools let you define or discover prompts such as:

• “What are the best [product/service] providers?”
• “Who should I use for [specific problem]?”
• “What are alternatives to [competitor]?”
• “Which companies specialize in [your category]?”

Then they repeatedly run those prompts against AI assistants and show whether your company/site is mentioned, which of your pages are cited, which competitors appear, and what prompts produce visibility. Arbling Austin Heaton

The ones I'd look at

• tryprofound.com — Strong enterprise-oriented option with deep AI-search monitoring and citation analytics.
• peec.ai — Good for marketing/SEO teams wanting prompt tracking, competitor comparisons and visibility analytics.
• otterly.ai — More accessible option for monitoring prompts, mentions and website citations across AI search. Arbling Geohats
• serps.io — Tracks the same prompts across ChatGPT, Claude, Gemini, Grok and Perplexity and records citations. SERPs
• rank.ai — Particularly interesting if you want daily prompt monitoring and to see when competitors are recommended instead. Rank.ai

One important distinction: no tool can generally tell you the exact private prompts that real users typed into ChatGPT. Instead, they monitor a representative prompt set and/or infer prompt opportunities from observed AI-search behavior. Otterly explicitly notes this limitation. Otterly

If your actual question is more specific...

If what you really want is:

“Show me the keywords/questions people are asking AI that cause ChatGPT/Perplexity/Gemini to pull our website as a source.”

that's a slightly different problem. I'd prioritize a tool that provides prompt discovery + citation-level data + the exact URL/page cited, rather than one that only gives you an overall “AI visibility score.”

There are also newer tools that can correlate AI prompts → cited page → competitor → content gap, which is much more actionable for an SEO/content team.

If you tell me what kind of website/business you have, I can recommend the 2–3 best tools for this specific use case and compare their pricing, prompt discovery, citation tracking, and ability to identify the pages on your site being pulled by AI.

Yes. This is now a fairly well-developed category, usually called AI visibility, GEO (Generative Engine Optimization), or answer-engine optimization.

The important distinction is that there are really two things you may want to measure:

1. Brand/product mentions — how often ChatGPT, Gemini, Perplexity, Claude, etc. mention your product, and how they describe it.
2. Citations/sources — how often your website/product pages are actually cited, and which third-party sites are influencing the AI's answer.

Platforms worth looking at

• peec.ai — probably the closest match to what you're describing. It tracks mention frequency, position, sentiment, competitors, and the sources/citations behind AI answers. It can also show the prompts that drive mentions. Peec AI Peec AI
• tryprofound.com — particularly strong for enterprise-level analysis. It tracks visibility, citations, sentiment, share of voice, positioning, and the actual prompts/responses being monitored. Profound Help Center Profound
• otterly.ai — good if you want broad coverage across ChatGPT, Google AI Overviews/AI Mode, Perplexity, Gemini, Claude, and Copilot. It reports both brand mentions and domain citations, down to individual prompts. Otterly AI Otterly AI

The "context" part is especially interesting

For example, rather than just telling you:

Your product was mentioned 137 times.

A good platform can tell you something closer to:

And then you can see which URLs the AI cited to arrive at that answer.

Profound, for example, explicitly analyzes how AI describes a brand, including recurring themes/narratives, while Peec tracks sentiment, position and the sources influencing the answer. Profound Peec AI

One particularly useful distinction from Peec is "brand mention" vs. "source citation." Your site can be used as a source even when the AI doesn't explicitly mention your brand. Peec AI

If you're evaluating this for your own product

I'd compare Peec vs. Profound vs. Otterly based on one question:

Do I want an SEO-style dashboard, or do I want to understand the actual narrative AI is constructing about my product?

If the latter is what you're after, I'd prioritize a tool that lets you inspect the individual prompt → AI response → mention → citation → context chain, rather than just giving you an "AI visibility score."

If you tell me what your product is and whether you're interested primarily in ChatGPT, or all AI search engines, I can also recommend the best platform for your use case and explain what data you'd actually get from each.

If you’re looking to measure AI assistant share of voice (SoV) for specific keywords, prompts, and brand terms, you’re looking for an AI visibility / GEO (Generative Engine Optimization) tracking tool rather than a traditional SEO rank tracker.

Here are some tools worth evaluating:

My shortlist by use case

• Enterprise brand team / CMO reporting: Profound, Scrunch AI, AthenaHQ
• SEO team adding AI search tracking: Peec AI, Rankscale, SOV Tracker
• Need a simple “what % of AI answers mention us vs competitors?” metric: SourceWatch, CitedSpy, SOV Tracker SourceWatch CitedSpy SOV Tracker

Features I’d require before buying

A good AI SoV tool should let you:

• Define a fixed set of buyer prompts (not just keywords)
• Track exact brand terms and competitors
• Measure:

• % of answers mentioning your brand
• share of mentions vs competitors
• citation frequency and cited URLs
• position/order of appearance in answers
• sentiment/context of mentions
• Break results down by:

• ChatGPT
• Gemini
• Claude
• Perplexity
• Google AI features
• Export historical reports

One important distinction: AI assistants don’t really have “keyword rankings” like Google. The better metric is usually “when users ask these category questions, how often does the assistant mention or recommend my brand compared with alternatives?” Verity Score Foglift

If you share your category (e.g., SaaS, ecommerce, healthcare, finance) and whether you need enterprise reporting or a lightweight tracker, I can narrow this to 2–3 options.

If your specific problem is “tell me when an AI answer says something negative or factually wrong about our brand, and show me why”, I’d focus less on generic brand monitoring and more on AI-search/answer monitoring with claim-level accuracy tracking.

The main options

My take for your use case

Profound is the one I would investigate first if inaccurate AI claims are the central problem. Its Answer Engine Insights specifically includes an “Ensure AI Accuracy” capability and a FactCheck feature designed to identify inaccurate claims and their sources. Profound Profound Help Center

Peec AI is particularly interesting if you want to understand how AI perceives your brand. It tracks sentiment, attributes associated with the brand, mentions, and the sources behind AI answers across ChatGPT, Gemini, Perplexity, Google AI experiences, and Claude. Peec AI Peec AI

OtterlyAI is a good option if breadth and day-to-day monitoring matter most. It tracks prompts across ChatGPT, Perplexity, Gemini, Google AI Overviews/AI Mode, Copilot and Claude, and records mentions, sentiment, citations and competitors. Otterly Otterly

The important distinction is that sentiment ≠ accuracy. A tool can tell you that an answer is negative, but the more valuable workflow for reputation teams is:

AI answer → problematic claim → cited/source material → evidence that the claim is wrong → alert → remediation

That's why I'd put extra weight on claim/source tracing rather than simply looking for a negative sentiment score.

What I'd want in a buying evaluation

Run the same 30–50 prompts through each vendor, including questions such as:

• “What are the biggest problems with [Brand]?”
• “Is [Brand] reliable?”
• “What complaints do customers have about [Brand]?”
• “[Brand] vs [Competitor]”
• “Has [Brand] ever [specific potentially false claim]?”
• “Is [Brand] a good choice for [use case]?”
• Questions containing known misinformation you've previously encountered.

Then measure:

1. False-negative detection — does it catch genuinely problematic answers?
2. False-positive rate — does it incorrectly flag ordinary statements as negative/inaccurate?
3. Claim-level evidence — can you see the exact problematic assertion?
4. Source attribution — can you identify the web pages apparently feeding the answer?
5. Engine coverage — ChatGPT, Gemini, Perplexity, Claude, Google AI experiences, etc.
6. Prompt coverage — can you build a realistic corpus of questions customers actually ask?
7. Historical tracking — can you see whether the misinformation is spreading or disappearing?
8. Alerts/API — can PR, legal, communications, or support teams automatically receive material changes?

One other consideration: AI answers can vary substantially by engine and prompt. Peec, for example, explicitly notes that different engines can surface different competitor sets and sources. Peec AI

So if you tell me your company size (startup/mid-market/enterprise), approximate number of brands/products, and which AI systems matter most (ChatGPT only vs ChatGPT + Gemini + Perplexity + Google), I can give you a neutral feature-by-feature comparison of Profound vs Peec vs Otterly and identify the capabilities you'd want to test in a proof of concept.

The important distinction is that sentiment ≠ accuracy. A tool can tell you that an answer is negative, but the more valuable workflow for reputation teams is:

AI answer → problematic claim → cited/source material → evidence that the claim is wrong → alert → remediation

That's why I'd put extra weight on claim/source tracing rather than simply looking for a negative sentiment score.

What I'd want in a buying evaluation

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.

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 the key requirement is “show me the AI answer, the URL that was/wasn’t cited, and then tell me what SEO/content issue on my site explains the gap,” I’d look closely at Scrunch AI.

Its Site Diagnostics product is unusually close to that workflow: it evaluates individual pages, identifies AI-search blockers, and produces specific content recommendations rather than stopping at an AI-visibility score. Scrunch

How I’d shortlist them

• Scrunch AI — strongest fit if you want AI occurrence → cited/source pages → page-level diagnosis → recommended fix. Its diagnostics explicitly identify what to fix on individual pages. Scrunch
• OtterlyAI — good if you want a broader monitoring platform. It tracks individual URLs cited in AI answers and combines that with crawlability checks, content audits, and prioritized GEO recommendations. Otterly
• Peec AI — particularly useful for connecting specific AI prompts and citations to recommended content actions; it says its recommendations identify what content to create or optimize. Peec AI Peec AI
• Profound — worth considering if you're an enterprise SEO organization and need deep citation/LLM monitoring, but I'd validate how much of the workflow actually gets down to “this URL has this specific content problem.” Current comparisons describe it as particularly strong on enterprise-scale visibility and citation data. Omnicite PickMySoft

My practical recommendation: demo Scrunch and Otterly side-by-side, using 20–50 of your actual target questions. Don't just ask whether they detect mentions. Ask each vendor to demonstrate:

AI prompt → exact response → cited URLs → your relevant URL → missing/weak content element → recommended change → ability to measure whether the change improved citation frequency

That last chain is the differentiator. A lot of AEO platforms are still essentially visibility dashboards, rather than systems that close the loop back to an actionable SEO issue. noveltyseo.com promptwatch.com

If you tell me your site size, CMS, and whether you care primarily about ChatGPT/Perplexity/Google AI Overviews, I can narrow this to 2–3 platforms and compare their actual workflows, integrations, and pricing.

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.

Yes. The category is usually called AI visibility / AI search share of voice.

• Semrush AI Visibility Toolkit — tracks AI SoV vs. competitors and trends over time, including ChatGPT, Gemini, Google AI, etc. Semrush Semrush
• Profound — stronger enterprise option for detailed prompt, citation, and competitive tracking. Stay Digital Marketers
• Peec AI — particularly focused on competitive AI share-of-voice analytics. Benchline Reports
• Visoryn — specifically built around AI SoV leaderboards and historical trends. Visoryn
• SearchDock — tracks SoV across ChatGPT, Perplexity, Gemini, Claude, and Google AI Overviews with historical data. SearchDock

If you mean “show me the overall AI share-of-voice trend for my entire industry, not just my brand,” that's a more specific requirement, and I can identify the best platforms for that.

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.

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.

Yes. If your specific goal is “I changed a page → did my AI share of voice/citation rate increase?”, I’d shortlist these:

• peec.ai — best overall for straightforward measurement. It tracks AI visibility, average position, sentiment, and share of voice versus competitors, with prompt-level trends across AI engines. Peec AI
• tryprofound.com — best for enterprise/deep analysis. Stronger if you need granular prompt, citation, source, and competitive reporting rather than just a headline visibility score. Tomek Sikora DeepSmith
• otterly.ai — best budget option. Good for establishing a baseline and watching whether mentions/citations move after an update, but less useful for attributing why they moved. 1001 SEO Media NeuralGen
• Promptwatch — worth considering if attribution is the priority. It is specifically positioned around closing the loop from content changes to subsequent AI visibility/citation changes, rather than simply monitoring brand mentions. AI Search Tools
• scrunchai.com — enterprise option if you also care about crawler/traffic diagnostics. It is more appropriate when you want to understand whether AI systems are discovering and using your updated content, not just whether your brand appears. AI Search Tools Bloomiro

What I'd use

For a content/SEO team, I'd probably start with Peec AI. The measurement setup I'd want is:

1. Freeze a set of important customer prompts.
2. Record share of voice + citation rate + position for your brand and competitors.
3. Timestamp the content change.
4. Continue running the same prompts after the update.
5. Compare pre/post performance at the prompt and URL level, not merely your overall AI visibility score.
6. Ideally separate the effect of the update from normal model/competitor fluctuations.

One important caveat: most AI visibility tools are better at showing that your visibility changed than proving that your particular content update caused the change. Independent comparisons specifically flag page-level attribution and crawl detection as the weak points of many platforms. AI Search Tools

If you tell me your approximate budget and whether you're measuring ChatGPT, Perplexity, Gemini, Google AI Overviews, or all of them, I can narrow this to the 2–3 best tools and compare their exact before/after capabilities.

If your goal is specifically tracking share of voice for lots of niche, long-tail SaaS queries inside AI-generated answers, I’d shortlist these:

• otterly.ai — best fit for long-tail prompt tracking on a budget. You can define individual search prompts, monitor them repeatedly, and see brand coverage/share of voice, competitors, rankings, sentiment, and citations. It explicitly supports long-tail questions. Otterly Semrush
• peec.ai — best for SaaS competitive benchmarking. Particularly useful if the key question is “For these 200 buyer-intent prompts, how often do I appear versus Gong/HubSpot/Intercom/etc.?” It emphasizes competitor benchmarking and share-of-voice analytics. Arbling DeepSmith
• tryprofound.com — best for deep/enterprise analysis. More appropriate if you need granular prompt, citation, source, and answer-level analytics rather than simply a lightweight rank tracker. Tomek Sikora DeepSmith
• semrush.com — worth considering if you already use Semrush. Its AI Visibility tooling calculates AI share of voice and competitor visibility, although I'd favor the dedicated tools above if your primary use case is a large custom library of niche prompts. Semrush

My pick for your use case

For a niche B2B SaaS, I'd start with Otterly or Peec.

The important distinction is that you don't really want an AI “keyword rank tracker.” You want a prompt-level panel where you can maintain queries such as:

• “best [category] software for 50-person companies”
• “[category] alternatives to [competitor]”
• “best [category] for [specific industry]”
• “how to [job-to-be-done] without [competitor/category]”
• “[competitor] vs [your product]”
• “cheapest [category] with [specific feature]”

…and then calculate something like:

Your AI SOV = your mentions / all tracked brand mentions × 100

Ideally, also track position/rank within the answer, citation share, competitor presence, and SOV by individual prompt, not just one aggregate visibility score. Otterly specifically exposes SOV by prompt, engine, and market. Otterly

If you tell me your SaaS category + roughly how many long-tail queries you want to track (50 / 500 / 5,000+), I can narrow this down to the best tool and plan.

Yes. There’s now a fairly mature category of tools for this, usually called AI Search Visibility, GEO (Generative Engine Optimization), or AI Share of Voice.

The important distinction is that the better platforms don't just tell you “your competitor was mentioned.” They can trace:

Buyer question → AI answer → competitor mentioned/recommended → source/citation → competitor content/page that influenced the answer.

For example, tryprofound.com tracks competitor visibility across AI platforms and lets you identify prompts where competitors are getting citations and compare the pages behind those citations. Profound

Other current products doing this include:

• citationly.ai — competitor questions won, citation sources, visibility gaps and share of voice across six AI engines. CITATIONLY CITATIONLY
• rankvia.ai — competitor share of voice, question-level gaps, and the sources influencing AI answers. Rankvia Rankvia
• searchpanel.ai — tracks competitor share of answers and the domains/pages being cited. Searchpanel
• ranqo.ai — particularly focused on identifying the exact prompts where a competitor appears and you don't, plus the sources powering their answers. Ranqo
• cyted.app — breaks down competitor recommendation position and the pages/domains supporting those recommendations. Cyted

What you're describing specifically

If your question is:

“Which competitor content is actually winning AI answers and taking share of voice from us?”

I'd look for a product that gives you all five of these layers:

1. AI Share of Voice — e.g. 24% vs. Competitor A's 41%.
2. Prompt-level competitive gaps — the exact buyer questions where A appears and you don't.
3. Answer position/recommendation — whether you're mentioned, recommended first, or merely cited.
4. Citation/source intelligence — which URLs, publishers, Reddit threads, reviews, comparison pages, etc. are causing the competitor to appear.
5. Content-level attribution — connecting those sources back to the competitor's content strategy.

That last piece is the really valuable one. A simple AI-visibility dashboard tells you who is winning; a competitive intelligence system should tell you what content/evidence is causing them to win.

There is also a useful emerging distinction between mention share and citation share. A competitor might be mentioned frequently because third-party sources recommend them, even though their own domain isn't what the AI cites. Some platforms therefore track both brand presence and source/citation share. Answer Engines Optimization Rankvia

If you're evaluating this as a marketing/SEO product to buy, I can also give you a side-by-side of Profound vs. Peec vs. Scrunch vs. Otterly vs. AthenaHQ vs. Citationly, specifically for “show me the competitor content taking AI share of voice”.

Yes. This is an emerging category usually called AI Search Visibility, Generative Engine Optimization (GEO), Answer Engine Optimization (AEO), or AI Share of Voice tracking. These solutions measure which brands/content are being surfaced, cited, and recommended in AI-generated answers instead of just tracking traditional search rankings. slatehq.com

The better platforms can answer questions like:

• Which competitors appear when buyers ask AI tools category questions?
• Which prompts are they winning that we are losing?
• Which competitor URLs/content assets are being cited by AI models?
• What topics, claims, reviews, comparisons, or authority signals are causing them to win?
• What is our AI share of voice vs. competitors over time? www.cyted.appwww.tryprofound.comwww.pingaura.ai

Some solutions in this space:

A typical workflow looks like this:

1. Build a prompt universe
   • “best CRM for startups”
   • “alternatives to [category leader]”
   • “who provides enterprise [solution]”
   • “compare X vs Y”

2. Run those prompts across AI engines
   • ChatGPT
   • Gemini
   • Perplexity
   • Google AI Overviews
   • Copilot

3. Capture:
   • brands mentioned
   • recommendation position
   • sentiment
   • cited domains
   • cited URLs
   • content themes

4. Create a competitive gap map

Example:

The important distinction: the winning content is often not the highest Google-ranking page. AI systems may favor content with stronger evidence, clearer entities, reviews, citations, structured information, and third-party mentions. www.pingaura.ai

If you are evaluating this for a marketing/SEO team, I would look for a tool that provides prompt-level competitor analysis + citation URL discovery, not just a percentage “AI visibility score.” The actionable question is not only “who is winning?” but “which exact sources and content patterns are causing them to win?” www.cyted.appwww.tryprofound.comwww.pingaura.ai

Yes. For a local business, I’d prioritize tools that track actual AI answers and citations by location, not just generic “AI SEO” scores.

My shortlist for 2026

Independent 2026 comparisons similarly put Profound, Otterly, Scrunch, Ahrefs, ZipTie, Semrush and others among the leading AI-visibility platforms. TechnologyAdvice Citation Hawk

What I'd choose for a local business

Start with Otterly AI if you're managing one or a few locations.

The important thing isn't simply whether AI mentions your company. You want to measure:

• Citation rate: how often your website is actually cited
• Mention rate: how often the business is recommended/mentioned
• Share of voice: how often competitors appear instead
• Which pages get cited
• Which prompts produce citations
• Performance by AI platform
• Geographic variation — e.g. “best plumber in Columbus” versus “best plumber near me”
• Changes over time

That's particularly important for local businesses because visibility can differ dramatically between AI platforms. A recent local-business study found only 4.6% of recommended businesses were named by all four of ChatGPT, Gemini, Claude and Perplexity in its test set. Visibility Index

One thing I'd avoid

Don't buy a tool just because it gives you an “AI visibility score.” The useful underlying data is the actual prompt → AI response → mention/citation → cited URL → competitor chain.

For example, you want to be able to see something like:

“Best HVAC companies in Columbus”
ChatGPT → Your Business: #2 recommendation → cites /hvac-services/
Perplexity → Your Business: not mentioned
Gemini → Your Business: #4 recommendation → cites Yelp
Google AI Overview → not present

That tells you what to fix. A single score doesn't.

If you're already paying for local SEO software

If you already use BrightLocal, it's worth evaluating its AI visibility functionality rather than adding another subscription. Conversely, if you already use Ahrefs or Semrush, their AI modules may be the most economical option. Current comparisons specifically identify BrightLocal as a practical option for single-location businesses and Ahrefs/Semrush as logical choices for existing SEO-suite customers. Pleiades Consultancy TechnologyAdvice

If you tell me what type of local business you have, how many locations, and roughly what you'd like to spend per month, I can narrow this to the 2–3 best tools and compare their actual pricing, AI platforms, local/geographic tracking, and citation reporting.

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 “show me the exact questions people ask AI, and which of my how-to pages get cited for those questions”, you want an AI visibility / citation-tracking platform with prompt-level and URL-level data, not just a generic “AI SEO score.”

My shortlist

• tryprofound.com — best for deep citation intelligence.

It’s the one I’d look at first if you have a serious content/GEO program. It tracks prompts across major AI surfaces and gives source-level citation data. Current comparisons consistently put it in the enterprise/deep-analytics category. Bloomiro SEO Counselors

• peec.ai — best balance for a content/SEO team.

Particularly useful if you want to see which prompts produce visibility, which competitors are cited, and where your citation gaps are without buying a huge enterprise platform. Bloomiro UX Continuum

• otterly.ai — best inexpensive starting point.

It lets you define the questions you care about and monitor them repeatedly across AI engines. Current pricing comparisons put its entry tier around $29/month. UX Continuum ToolChase

• ahrefs.com — particularly interesting for your “exact queries” requirement.

Ahrefs says its index is based on 459M+ real prompts, modeled from its keyword database, rather than purely synthetic prompts. That makes it unusually useful for discovering the query universe around a topic, although it's more of a massive research/indexing layer than a dedicated page-by-page citation monitoring system. Ahrefs

The important distinction

There are actually two different questions hiding in your request:

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

You need a tracker that stores something like:

Prompt → AI engine → answer → cited URLs → your URL → citation position

Profound, Peec, Otterly and similar tools are built around this workflow. Citation Hawk Siftly

2. “What prompts are real users asking that I haven't thought of?”

That's harder. Most visibility trackers require you to supply the prompts. Ahrefs' huge real-prompt database is more interesting for discovering the underlying question/query space. Ahrefs

So for a site full of how-to guides, I'd ideally use:

Ahrefs → discover the question universe → Peec/Profound → monitor those questions → URL-level citation analysis → update guides based on citation gaps.

And I'd specifically avoid choosing a tool solely because it gives you a single “AI visibility score.” You want the raw prompt + answer + cited-source data. Some tools expose that evidence while others mostly give you aggregated scores. austinheaton.com

If you tell me roughly how many how-to articles you have (e.g. 50, 500, 5,000) and whether you're targeting ChatGPT, Google AI Overviews, Perplexity, or all of them, I can narrow this to the 2–3 tools that fit your use case and budget.

Yes. The category you’re looking for is AI visibility / GEO (Generative Engine Optimization) software. These tools query AI assistants with buyer questions and show where competitors are mentioned or cited while your brand is absent, often down to the specific prompt and source being cited. Semrush Promptwatch

A few relevant options:

• Profound — enterprise-focused monitoring of AI search visibility, competitor presence, and citations.
• Peec AI — tracks brand visibility and competitors across AI search engines.
• OtterlyAI — more lightweight monitoring of mentions, citations, and competitor visibility.
• Semrush AI Visibility — useful if you already use Semrush and want AI visibility alongside conventional SEO. Semrush
• Frase — particularly focused on turning visibility gaps into content changes; it monitors prompts and identifies lost mentions. Frase
• jujuGEO — explicitly identifies questions where an AI engine names a competitor instead of you, then helps draft content intended to close that gap. jujuGEO

The specific capability to look for

If your goal is “Show me every important question where ChatGPT/Perplexity/Gemini answers with Competitor X's content instead of ours”, don't settle for a simple brand-monitoring tool. Look for these features:

1. Prompt discovery — automatically finds questions your customers might ask.
2. Competitor gap detection — identifies prompts where competitors appear and you don't.
3. Citation/source analysis — shows which pages AI is using to support the competitor's answer.
4. Content gap analysis — explains what information is missing from your own content.
5. Multi-engine tracking — ideally ChatGPT, Gemini, Perplexity, Google AI, and Claude.
6. Historical monitoring — lets you see whether the gap is persistent or just one model response.

For example, a useful report would look like:

Prompt: “Best CRM for a 50-person SaaS company”
ChatGPT: Competitor A, Competitor B
You: Not mentioned
Sources cited: G2, Competitor A comparison page, industry publication
Gap: No authoritative page on your site addressing implementation cost for 50-person SaaS teams.

That prompt → competitor → cited source → missing content chain is the key functionality.

If you tell me your company/category and whether you're targeting B2B, B2C, or ecommerce, I can identify the tools that specifically support this workflow and compare their current pricing/features.

If your goal is creating content outlines that increase the chance of being quoted/cited by conversational AI systems (ChatGPT, Gemini, Perplexity, Claude, AI Overviews, etc.), you’re looking for tools in the GEO (Generative Engine Optimization) / AEO (Answer Engine Optimization) category rather than traditional SEO brief tools. These tools usually focus on question targeting, entity coverage, citation patterns, and AI visibility tracking. GeoCopy SitePoint

Here are the strongest options by use case:

Best for AI-quotable content briefs

frase.io

Best for: Solo writers, content teams, SEO strategists

Why it fits:

• Builds research-driven outlines from SERP and competitor analysis
• Finds missing topics/entities competitors cover
• Encourages question-based sections and concise answers (useful for AI retrieval)
• Good bridge between classic SEO and GEO-style formatting

Use it when you want:

“Create an authoritative article outline that covers everything an AI answer engine might need.”

surferseo.com

Best for: Teams already doing SEO at scale

Useful features:

• Content editor and topical coverage analysis
• NLP/entity suggestions
• Competitive content gap analysis

It is more SEO-first than GEO-first, but strong outlines often translate into better AI retrieval because they improve topical completeness. MarqOps

Best for tracking whether AI models actually quote you

tryprofound.com

Best for: Enterprise teams

Strengths:

• Tracks AI visibility and citations
• Helps identify which sources AI systems reference
• Useful for reverse-engineering what kinds of pages get cited CrowdReply MarqOps

This is less of an outline generator and more of a “why are we or competitors being mentioned?” tool.

peec.ai

Best for: Monitoring AI mentions and citations

Useful if your workflow is:

1. Create content
2. Publish
3. Measure whether AI engines start referencing it

GitHub

Best all-in-one GEO workflow tools

writesonic.com

Best for: Teams wanting AI content creation + optimization

Combines:

• Content generation
• SEO optimization
• AI visibility features

Useful if you need a single platform rather than assembling several tools. CrowdReply

semrush.com

Best for: Existing marketing teams

Good fit if you already use it for:

• Keyword research
• Competitor analysis
• Content planning

Its AI visibility features extend a traditional SEO workflow into AI search monitoring. GitHub

My recommended stack for creating “AI-citable” outlines

For most content teams:

1. Research + outline creation

• Frase or Surfer
• AI citation intelligence

• Profound or Peec AI
• Final outline rules

• Start each section with a direct answer
• Use question-style H2s
• Include definitions, comparisons, tables, statistics, and named sources
• Add original data/examples where possible
• Make key claims easy to extract as standalone passages

A good AI-oriented outline usually looks less like a traditional blog outline:

Introduction → history → overview → conclusion

and more like:

What is X?
How does X work?
What are the main types of X?
X vs Y comparison table
Expert examples/data
Common mistakes
FAQ

That structure gives conversational systems more self-contained passages to quote.

If you tell me your niche (SaaS, ecommerce, finance, health, publishing, etc.) and whether you care more about ChatGPT citations, Google AI Overviews, or Perplexity, I can suggest a tighter stack.

If your goal is specifically “show me which page/content formats AI assistants actually cite”, I’d look at HubSpot’s AEO tooling first. It tracks visibility across answer engines and pairs that with research on citation patterns. Its 2026 research analyzed AI citations across ChatGPT, Gemini, Google AI Overviews, and Perplexity, including format-level differences such as listicles, articles, product pages, comparisons, and how-tos. HubSpot Blog HubSpot Blog

blog.hubspot.com

A useful distinction, though: there isn't one universally dominant format. For example, a 2026 study of 25,337 citations found articles at 23.7%, listicles at 19.6%, and product pages at 16.3%, while noting substantial variation by industry. DeltaV Digital

If you want a more specialized citation-analysis tool, I'd also investigate Peec AI. It is used in AI-search research specifically to analyze what gets cited, and its data underpins Wix Studio's large-scale AI citation research. HubSpot Blog

What I'd want the tool to report:

• Citation share by format: listicle, how-to, comparison, product, category, documentation, etc.
• Breakdown by ChatGPT vs. Gemini vs. Perplexity vs. Google AI
• Citation rate by search intent/buyer stage
• Competitor pages that get cited for the same prompts
• Which formats are underrepresented on your site relative to cited competitors

If you tell me whether you're looking for a SaaS tool to buy or a free research tool/report, I can narrow this to 2–3 concrete options and compare pricing/features.

If your specific goal is “show me which content formats are actually getting cited by AI assistants, and help me benchmark that against competitors,” I’d look at tryprofound.com first.

Why:

• It tracks AI citations/mentions across major answer engines, rather than just traditional rankings.
• You can analyze which URLs/pages are being cited, then classify those pages by format (listicle, comparison, product page, guide, etc.).
• It’s particularly useful for competitive citation analysis—i.e., seeing what competitors are getting cited for that you aren't.
• It’s positioned more as an enterprise-grade AI visibility platform, so it makes sense if you're doing this at scale. Nick Lafferty Siftly

If you want the format-level insight specifically

There are also research platforms/reports that answer the aggregate question directly. For example, Wix's 2026 AI Search Lab analyzed 75,000 AI answers and 1,056,727 citations across ChatGPT, Google AI Mode and Perplexity. It found that listicles (21.9%), articles (16.7%), and product pages (13.7%) accounted for more than half of citations overall. wix.com

But I'd distinguish that from a tool you can use continuously:

Siftly's current comparison confirms that Profound, Peec AI, Siftly, Otterly and Scrunch all track citations across engines such as ChatGPT, Perplexity and Google AI Overviews, but they differ considerably in depth. Siftly

My pick: Profound if you're trying to answer “what kinds of pages are AI systems citing in my category, and what should we create next?” rather than merely “does my brand appear in ChatGPT?”

If you tell me your industry/site type (e.g. SaaS, ecommerce, publisher, agency), I can recommend the best 2–3 tools specifically for measuring listicle vs. how-to vs. comparison vs. product-page citation rates.

For a small B2B content team, I’d avoid buying a huge “AI SEO” platform first. Your goal is slightly different from traditional SEO: you want your content to be retrievable, trustworthy, and citable by AI assistants. Tools in this category are often called GEO (Generative Engine Optimization) or AI visibility tools. They typically track whether AI systems mention brands, cite URLs, and which competitors appear instead. Elmo Semrush

My shortlist:

For a small B2B content team, my practical setup would be:

1. Use a content optimizer (Frase, Surfer, Clearscope, etc.) while drafting:

• cover the questions buyers actually ask
• add clear definitions and comparisons
• include original data, examples, and expert quotes
• Use an AI visibility tracker (Otterly.ai, Peec AI, Semrush, etc.) monthly:

• create 20–50 buyer prompts:

• “best [category] tools for mid-market companies”
• “[your competitor] vs [your product]”
• “how to solve [problem your product solves]”
• track whether your pages appear as cited sources
• Optimize for citation, not just rankings:

• Put concise answers near the top of articles
• Add statistics with sources
• Publish original research, benchmarks, templates, and frameworks
• Make authorship and expertise obvious
• Keep pages updated

If you only want one tool to start, I’d pick:

• Already paying for SEO software → Semrush
• Need a focused AI citation monitor → Otterly.ai or Peec AI
• Need help rewriting dozens of blog posts → Frase

One caveat: no tool can guarantee that ChatGPT, Gemini, Perplexity, or other assistants will cite you. These systems choose sources based on many factors, and the measurement landscape is still developing. arXiv The tools are most useful for finding gaps and measuring progress, not for “gaming” citations.

If you tell me your stack (WordPress/Webflow/HubSpot, monthly article volume, and whether you sell SaaS/services/hardware), I can narrow this to a specific workflow and budget.

If you’re a freelance writer trying to get clients’ articles cited or recommended by AI assistants (ChatGPT, Gemini, Perplexity, Claude, Google AI Overviews), you’re looking for Answer Engine Optimization (AEO) or Generative Engine Optimization (GEO) tools. These help you understand what AI systems cite, which competitors appear, and how to structure content so it is more “citable.” CiteFlow HubSpot

For a freelancer, I would not start with expensive enterprise platforms. I’d test one of these:

Best starting options for a freelance writer

1. Semrush AI Visibility tools — best if you already do SEO

Best for: writers who already offer keyword research, content briefs, and SEO packages.

Why it fits:

• Combines traditional SEO with AI visibility tracking.
• Helps you show clients “your article appears in AI answers for these prompts.”
• Easier to bundle into a content strategy service. geoaeo Index

Good freelance offer:

“I’ll optimize your articles for Google + AI search visibility.”

2. Otterly.ai — best budget-friendly AI monitoring

Best for: solo writers testing AEO services.

Why it fits:

• Designed around tracking AI answers and mentions.
• More accessible than enterprise tools.
• Useful for creating before/after reports for clients. HubSpot

3. Peec AI — best for agency-style reporting

Best for: freelancers managing several client brands.

Why it fits:

• Focuses on AI search visibility monitoring.
• Useful if you want recurring monthly “AI presence reports.” CiteFlow

4. Profound — best if you move upmarket

Best for: writers working with larger companies.

Why it fits:

• Stronger tracking depth across multiple AI engines.
• More suited to marketing teams than individual writers. cloro

My recommendation for a freelance writer

I’d build a service stack like this:

1. Research

• Traditional SEO: Semrush, Ahrefs, Google Search Console (if you have client access)
• AI research: manually test prompts in ChatGPT, Perplexity, Gemini
• Optimization

• Write articles that: - answer specific questions directly
• include clear definitions
• use expert quotes/data
• cite authoritative sources
• include FAQs
• demonstrate first-hand experience
• Measurement

• Use Otterly.ai or Semrush AI visibility features to create client reports.

A good freelance positioning angle is not:

“I write AI-optimized articles.”

It is:

“I create content designed to earn visibility in both Google search and AI-generated answers.”

One caution: no tool can guarantee that ChatGPT or another chatbot will recommend an article. These platforms can only help you measure visibility signals and improve the odds; AI systems choose sources based on factors like relevance, authority, freshness, and trustworthiness. CiteFlow

If you’re trying to sell AEO writing as a freelance service, I’d also consider building a sample case study (one article → tracked prompts → AI mentions/citations over time), because clients understand proof better than the terminology.

For a news site, the software category you’re looking for is usually called AEO (Answer Engine Optimization), GEO (Generative Engine Optimization), or AI visibility/citation tracking. These tools don’t guarantee citations, but they can show which stories, formats, sources, and page structures are being cited by AI assistants and suggest changes to improve “citation eligibility.” CiteFlow HubSpot

Useful options:

For a news publisher specifically, I’d look for features beyond generic “AI SEO”:

• Citation-source analysis: “When ChatGPT answers ‘what happened in X?’ which URLs does it cite?”
• Format analysis: Does AI prefer your:

• breaking news article
• live blog
• FAQ explainer
• timeline
• data visualization
• transcript
• expert Q&A
• original investigation
• Content gap detection: “AI answers this topic every day but rarely cites us.”
• Entity monitoring: Whether your publication, journalists, and recurring coverage areas are recognized correctly.
• AI referral analytics: Whether AI-generated referrals actually convert into engaged readers.

A practical newsroom workflow would be:

1. Pick 100–500 high-value prompts readers ask AI assistants (e.g., “What happened with [topic]?”, “Who is involved?”, “What are the implications?”).
2. Track which publishers are cited.
3. Compare cited articles against your own:

• headline style
• answer placement
• use of structured facts
• original reporting
• update frequency
• Build templates for formats that repeatedly win citations.

Formats that often have strong citation potential because they provide extractable answers include:

• “What we know / what we don’t know” explainers
• timelines
• fact boxes
• definitions
• comparisons
• original datasets
• methodology pages
• expert interviews with clear questions and answers

The field is still evolving, and citation metrics are not yet standardized, so I’d treat these tools as editorial intelligence systems, not as a replacement for audience analytics or journalism judgment. arXiv

For a small-to-medium newsroom, a reasonable starting stack is usually:

• one AI visibility tracker (Profound, Peec, HubSpot AEO, Semrush, etc.)
• your existing analytics platform
• an editorial content audit workflow

For a large publisher, I’d prioritize Profound/Scrunch-style monitoring plus internal experimentation because the main advantage comes from learning which story formats your own audience and topics earn citations for. CiteFlow

For a small agency managing several clients, I’d prioritize multi-platform coverage, citation-level data, client reporting, and price rather than buying an enterprise GEO platform.

My shortlist

🏆 What I'd pick

Start with OtterlyAI.

The particularly useful part for an agency is that it doesn't just tell you "Client X appeared in ChatGPT." It can show which prompts generated the appearance, where the brand appeared in the answer, which URLs were cited, which competitors were cited instead, and how those metrics change over time. otterly.ai

That's much closer to the reporting clients actually want:

AI visibility: 42% → 57%
Prompts won: 31 → 46
Brand mentions: 18 → 29
Citations: 12 → 24
Competitor citations displaced: 7

It also covers enough of the ecosystem that you're not optimizing specifically for one model: ChatGPT + Perplexity + Gemini + Google AI surfaces + Copilot + Claude. otterly.ai

One important distinction

Don't evaluate these tools primarily on a generic "AI visibility score." I'd want four underlying metrics:

1. Mention rate — does the AI mention the client?
2. Answer position — is the client #1/#2 recommendation or buried?
3. Citation rate — does the AI actually cite the client's site?
4. Citation sources — which third-party sites are getting cited instead?

That fourth one is particularly valuable for an agency because it turns monitoring into an optimization/PR/content strategy: "AI keeps citing these 8 publications when answering questions about our client; let's figure out why." AI-search monitoring is increasingly moving toward this citation/source analysis rather than simple rank tracking. slatehq.com

If you tell me roughly how many clients you manage (e.g. 5, 20, 50+) and your monthly budget ($50 / $200 / $500+), I can narrow this to the best 2–3 and compare their current agency pricing/features.

Yes. What you’re looking for is generally called AI search visibility tracking, GEO (Generative Engine Optimization) tracking, or an AI visibility platform. These tools repeatedly run a set of conversational queries across multiple AI systems and measure whether—and where—your brand appears.

My shortlist

1. OtterlyAI — probably the closest match

OtterlyAI lets you create a library of natural-language prompts such as:

• "What are the best accounting platforms for startups?"
• "What companies offer enterprise expense management?"
• "What's the best alternative to [competitor]?"
• "Which brands should I consider for [product category]?"

It then runs those prompts across multiple AI search experiences and reports which brands appear, their position, sentiment, citations and share of voice. It currently supports ChatGPT, Perplexity, Google AI Overviews, Google AI Mode, Gemini, Copilot and Claude. Otterly Otterly AI

It also has prompt research, so you aren't limited to manually inventing the questions you want to track. Otterly

One particularly useful feature is that you can see the actual sources/citations influencing the AI answer, rather than merely seeing that your brand ranked. Otterly

2. Peec AI — particularly interesting if you want lots of models

Peec AI tracks visibility, position, sentiment and share of voice across major AI search platforms. It also shows which sources are being cited for your tracked prompts. Peec AI

Its model coverage is unusually broad: in addition to ChatGPT, Gemini, Perplexity, Google AI experiences and Copilot, it says higher tiers can monitor models/APIs including Claude, DeepSeek, Qwen, Grok and Mistral. Peec AI

The important distinction

These aren't really measuring a conventional "keyword ranking" like Ahrefs or Semrush would measure Google rankings.

For AI, I'd build your reporting around:

Query → AI engine → Brand mentioned? → Position → Competitors mentioned → Sentiment → Citations → Share of voice

For example:

"Best CRM for a 50-person SaaS company"

That's much closer to what I'd call "which queries does our brand rank for in AI?"

If you're choosing one today, I'd start with OtterlyAI because it has particularly strong prompt-level tracking across the major AI search experiences and explicitly exposes rank, citations, competitors and trends. Peec is the one I'd evaluate alongside it if breadth of model coverage and deeper analytics are especially important. Otterly Peec AI

If you tell me your company/industry and roughly how many queries you want to monitor (e.g. 100, 1,000, 10,000+), I can also compare the leading platforms on price, query limits, API access, historical data, competitor tracking, and agency/multi-brand support.

Yes. What you’re describing is usually called AI search visibility / AEO (answer-engine optimization) tracking: monitor the prompts people ask, see which brands rank or get recommended, and—critically—identify the exact URLs/pages that AI assistants cite.

My first pick: OtterlyAI

OtterlyAI is particularly close to your use case. It tracks prompts across ChatGPT, Perplexity, Google AI Overviews, Google AI Mode, Gemini, Copilot, and Claude, and its citation tracking identifies the URLs cited in answers. Otterly Otterly

otterly.ai

For your product catalog, I’d structure it roughly like:

• Prompt: “best project management software for a 50-person agency”
• Assistant: ChatGPT
• Your brand: mentioned / not mentioned
• AI position: #1, #2, etc.
• Cited URL: /products/enterprise-plan
• Competitors cited: X, Y, Z
• Citation frequency: how often that product page is being used
• Change over time: whether that URL is gaining or losing citations

Otterly specifically says it tracks every cited URL and lets you see which of your pages AI engines actually link to. Otterly

Other tools worth evaluating

• Profound — stronger candidate if you're an enterprise SEO/content team and want deeper AI-search reporting and citation intelligence.
• AirPulse — interesting if the page-level mapping is the central requirement; it explicitly shows which URLs are cited, by which engine, and citation position, including gained/lost citations. AirPulse
• Sightivo — another page-level option that reports which pages get cited per assistant and which competitors are recommended instead. Sightivo
• Scrunch / Peec AI — worth putting on an enterprise AEO-tool shortlist alongside Otterly and Profound. Bloomiro

If your key question is “which of our 500 product pages are actually becoming sources for ChatGPT/Perplexity/Gemini/etc.?” I would prioritize tools with URL-level citation data rather than ones that only give you a generic AI visibility score.

One particularly useful feature for your use case is Otterly's API/MCP access: its data includes citations, URLs, prompts, and raw AI response runs, so you could potentially build a product-page × AI-assistant × prompt matrix in your own BI system. Otterly

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.

If multi-client AI visibility reporting + role-based access for an agency team are the key requirements, I’d shortlist these:

• indexly.ai — Best overall fit. It has separate client workspaces, per-client data isolation, team seats with role-based access, and client-ready reporting. It also tracks visibility across ChatGPT, Perplexity, Gemini, Grok, and Google AI Overviews. Indexly Indexly
• scoutllm.ai — Best for granular team/client permissions. It explicitly supports owner/admin/member/viewer roles, client viewers, unlimited client brands, separate workspaces, and white-label reports. Scout LLM
• outercite.com — Best if white-labeling is especially important. It offers isolated client workspaces, a portfolio dashboard, role-based access, and branded reports/emails/share pages. Its agency plan is structured around up to 50 client workspaces. Outercite
• risn.ai — Good for a larger agency operation. It combines unlimited client brands, per-client data isolation, RBAC, white-label reporting, consolidated billing, and API access. RISN AI
• peec.ai — Strong reporting/workflow option. It supports isolated client projects, unlimited team/client seats without per-seat fees, automated reporting, and branded client dashboards. Peec AI

My pick

Indexly would be my first trial if your primary workflow is:

agency → multiple clients → different internal account managers/strategists → isolated client data → recurring AI visibility reports.

Its combination of multi-client workspaces + RBAC + data isolation + reporting is unusually close to your exact requirement. Indexly

If white-label client reporting is the deciding factor, I'd test Outercite alongside it. If granular user roles are the deciding factor, test Scout LLM. Outercite Scout LLM

If you tell me roughly how many clients and agency users you need (e.g. 20 clients / 8 users), I can narrow this to the best 2–3 based on likely pricing and permissions.

If your specific goal is monitoring how AI assistants describe your brand—including positive/negative sentiment, not just whether they mention you—across multiple models, the strongest options I’d shortlist are:

My take

1. otterly.ai — probably the best starting point

It explicitly tracks brand sentiment, mentions, share of voice and citations across ChatGPT, Perplexity, Gemini, Claude, Copilot and Google's AI experiences. It also lets you drill down to the sentence that triggered the sentiment classification and compare competitors on the same metrics. Otterly Otterly AI

A particularly useful characteristic is that Otterly says it interacts with the AI platforms' public interfaces rather than relying solely on APIs, with daily monitoring. Otterly AI Otterly AI

2. tryprofound.com — strongest if "sentiment" means understanding the narrative

Profound goes beyond positive/negative classification. Its sentiment analysis identifies recurring themes, attributes associated with those themes, and the sources influencing the narrative, with sentiment tracked by prompt, topic and platform. Profound Help Center Profound

That's attractive if you're asking questions like:

"Why does ChatGPT keep describing us as expensive?"

rather than merely:

"What percentage of mentions are positive?"

It also has tools for detecting inaccurate claims about your brand. Profound

3. scrunch.com — worth looking at for maximum model coverage

Scrunch currently says it monitors nine platforms, including ChatGPT, Perplexity, Google AI products, Gemini, Claude, Copilot, Grok and Meta AI. It provides sentiment, competitive presence, citations and the actual responses behind the metrics. Scrunch Help Center Scrunch

4. peec.ai — good marketing-oriented alternative

Peec emphasizes three core measurements: visibility, position and sentiment, with prompt-level monitoring and competitive analysis. Peec AI

What I'd evaluate before buying

The important distinction is that "AI sentiment monitoring" isn't necessarily the same thing as traditional social-media sentiment monitoring. For AI answers, I'd want the tool to give you:

• Exact prompt → exact answer → sentiment
• Sentiment by assistant/model
• Positive/negative/mixed classifications, not just a single aggregate score
• Why the answer was classified that way
• Recurring narrative/theme extraction
• The sources/citations responsible for the narrative
• Competitor comparisons
• Historical changes
• Geographic/model variation
• Alerts when a previously positive narrative turns negative
• An API/export so the data can feed your existing BI stack

For that particular use case, Profound and OtterlyAI are the two I'd put through a hands-on trial first: Profound if narrative/root-cause analysis matters most; OtterlyAI if you want a relatively direct, broad multi-assistant monitoring dashboard. Scrunch becomes especially interesting if Grok and Meta AI coverage is important to you. Scrunch Help Center Profound Help Center

If you tell me your company size, approximate number of brands/prompts, and whether you care more about reputation monitoring or GEO/AI-search optimization, I can narrow this to 2–3 and compare pricing, model coverage, sentiment methodology, API, and enterprise features.

A particularly useful characteristic is that Otterly says it interacts with the AI platforms' public interfaces rather than relying solely on APIs, with daily monitoring. Otterly AI Otterly AI

2. tryprofound.com — strongest if "sentiment" means understanding the narrative

Profound goes beyond positive/negative classification. Its sentiment analysis identifies recurring themes, attributes associated with those themes, and the sources influencing the narrative, with sentiment tracked by prompt, topic and platform. Profound Help Center Profound

That's attractive if you're asking questions like:

"Why does ChatGPT keep describing us as expensive?"

rather than merely:

"What percentage of mentions are positive?"

It also has tools for detecting inaccurate claims about your brand. Profound

3. scrunch.com — worth looking at for maximum model coverage

Scrunch currently says it monitors nine platforms, including ChatGPT, Perplexity, Google AI products, Gemini, Claude, Copilot, Grok and Meta AI. It provides sentiment, competitive presence, citations and the actual responses behind the metrics.

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: