Neutral mentions of Peec AI

Yes. There’s now a fairly well-defined category for this: AI visibility / AI search monitoring / GEO tracking.

These platforms repeatedly run a set of prompts against ChatGPT, Perplexity, Gemini, Claude, Google AI Overviews, etc., then record whether your product is mentioned, cited, how it is described, and which competitors appear alongside it. Otterly SEO Counselors

Platforms worth looking at

• otterly.ai — probably the most straightforward fit. It tracks brand mentions and website citations across ChatGPT, Perplexity, Gemini, Claude, Copilot and Google's AI surfaces. It can show the underlying answers, cited URLs, prompt-level results, sentiment, and trends over time. Otterly Otterly
• citations.io — specifically oriented around whether/how often ChatGPT, Gemini, Perplexity and Claude mention your business. Its dashboard includes citation counts and the actual context behind mentions. Citations.io
• Profound — more enterprise-oriented, with deeper source/citation intelligence and larger-scale prompt monitoring. Current third-party comparisons put it toward the higher end of the market. SEO Counselors
• Peec AI — another dedicated AI-search visibility platform focused on monitoring prompts, brand mentions, citations and competitive share of voice. SEO Counselors Conbersa
• rank.ai — interesting if you specifically want repeated/daily measurements. It runs defined prompts across multiple AI providers and calculates the percentage of runs that mention or cite you. Rank.ai

The distinction I'd pay attention to

There are actually two different things you might mean by "cited":

1. Brand/product mention

"For project management, I'd recommend Acme."

2. Website/source citation

"Acme is a project-management platform..." [acme.com]

The second is much more useful if you're trying to understand whether AI systems are actually using your content as a source. Otterly, for example, explicitly tracks the URLs cited in AI answers and whether those citations are to your site or competitors' sites. Otterly

And context can be quite granular: you can track prompts such as "best X for small businesses," "alternatives to X," "X vs Y," or questions about particular product features, then see exactly how the AI describes your product and what it recommends instead. Otterly Rank.ai

One important caveat: these aren't generally measuring every conversation happening inside ChatGPT. They construct a representative set of prompts and repeatedly query the AI systems. Results can vary with model, location, personalization, prompt wording and time, so the useful metric is usually visibility over a consistent prompt set, rather than an absolute count of every time your product has ever been mentioned. Otterly

If you tell me what your product is and roughly how much you're willing to spend per month, I can compare the current options specifically for citation frequency + exact context + historical tracking, rather than just giving you a generic AI-SEO tool list.

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 monitoring multiple client brands across industries, I’d treat this as an agency-level AI visibility measurement problem, not simply a ChatGPT keyword tracker.

My recommendation: use Otterly as the monitoring layer

otterly.ai is the first platform I’d pilot for this use case. It is specifically built around recurring prompts and reports brand mentions, competitive visibility, citations, and share of voice across ChatGPT and other AI engines. Its current product supports multiple brands/projects and lets you segment prompts by intent and geography. Otterly

For an agency, I would build the operating model like this:

The important part: build a standardized prompt framework

Don't let each account manager invent prompts ad hoc. Create a master taxonomy such as:

• Category discovery: "What are the best [category] companies?"
• Best-of: "What are the best [product/service] for [audience]?"
• Comparison: "[Brand A] vs [Brand B]"
• Problem/solution: "What's the best way to solve [problem]?"
• Purchase intent: "Where should I buy [product]?"
• Local: "Who provides [service] in [location]?"
• Attribute-specific: "Which [category] has the best [attribute]?"
• Negative/concern: "What are the drawbacks of [category/brand]?"

Then assign perhaps 50–200 prompts per client, depending on account importance.

The key metric I'd report is:

AI Share of Voice = brand mentions ÷ total relevant brand mentions across the tracked prompt set

I'd keep mention share, citation share, and recommendation/position as separate metrics rather than collapsing everything into one opaque "AI visibility score."

For a larger agency, consider Peec or Profound too

peec.ai is worth testing if your priority is client-level competitive analytics and citation analysis. Current industry comparisons describe it as particularly suited to agencies/multi-project setups. Geodocs Conbersa

tryprofound.com becomes more interesting when clients are enterprise accounts and you need deeper analytics, governance, and broader AI-search monitoring. Current comparisons position it toward the enterprise end of the market. Mentn TechnologyAdvice

What I'd actually implement

Phase 1 — Pilot

Run Otterly + your own reporting layer for 3–5 clients representing very different industries.

For each client:

1. Define 50–100 high-value prompts.
2. Establish 5–10 competitors.
3. Run them on a consistent schedule.
4. Store raw AI responses, not just scores.
5. Calculate SOV by:

• industry/category
• intent
• prompt
• competitor
• geography
• AI engine
• Track cited URLs/domains.
• Flag meaningful changes.
• Produce a monthly client-facing report.

Phase 2 — Agency dashboard

Put all clients into one dashboard with:

Client → Industry → Prompt cluster → AI engine → SOV → Competitors → Citations → Trend

This is where I'd avoid relying entirely on the vendor dashboard. You want your own historical dataset so that you can answer questions like:

"Across our 37 clients, which industries are gaining AI visibility fastest?"

and:

"Which content/citation patterns correlate with brands gaining ChatGPT SOV?"

One important caveat

Don't treat ChatGPT SOV like traditional Google rank tracking. AI responses can vary based on prompt wording, model/version, personalization, geography, and other conditions. Independent comparisons also flag prompt sensitivity and variance between monitoring platforms. Geodocs Acromatico

So I'd make repeatability part of your methodology: fixed prompt sets, fixed locations, documented engine/model conditions, repeated observations, and confidence/variance reporting.

Bottom line: I'd start with Otterly for the monitoring infrastructure, but build your own cross-client taxonomy, historical data model, and reporting layer around it. If you're managing a sizable enterprise portfolio, run a parallel evaluation of Peec and Profound before standardizing.

If you tell me roughly how many client brands (e.g. 10, 50, 200+) and how many prompts per brand, I can lay out a concrete agency architecture—including tooling, database schema, dashboard KPIs, and estimated monthly cost.

If your goal is specifically enterprise-grade monitoring and auditing of how often a trademark/brand appears in conversational AI answers, you’re looking at the emerging AI visibility / AI search monitoring category. These platforms repeatedly run a controlled set of prompts through AI systems and measure mentions, position, competitors, citations, sentiment, and changes over time. Nadia Mohamed Peec AI

Main enterprise options

Third-party comparisons consistently put Profound, Peec, Otterly, AthenaHQ and Scrunch among the principal platforms in this category, although feature sets and pricing are changing quickly. Geodocs Refine AI

For an enterprise trademark-audit use case

I'd evaluate them less on a generic "AI visibility score" and more on whether they can answer these audit questions:

1. Mention rate: In what percentage of tracked answers does our trademark appear?
2. Engine/model: Does that rate differ between ChatGPT, Gemini, Claude, Perplexity, Google AI Overviews/AI Mode, Copilot, etc.?
3. Prompt universe: Can we maintain a controlled, versioned library of thousands of queries around our products, categories, competitors and trademarks?
4. Exact evidence: Can we retain the actual AI response that contained the trademark, rather than just reporting a score?
5. Citation provenance: Which websites/sources caused the AI to mention us?
6. Competitive share: How often are our trademarks mentioned versus specified competitors?
7. Context: Is the trademark being mentioned as a product recommendation, comparison, alternative, factual reference, etc.?
8. Time series: Can legal/brand teams demonstrate that mention frequency changed from one period to another?
9. Geography/language: Can the same audit be run by country and language?
10. API/export: Can raw observations feed your existing brand-monitoring, BI, legal or compliance systems?

For example, Otterly says it stores the underlying AI answers and analyzes who was named, ordering, tone, and cited pages, while also providing daily monitoring. OtterlyAI Otterly Peec explicitly reports mention frequency per engine over time alongside position, sentiment and citations. Peec AI

One important distinction: these products generally measure AI visibility, not trademark infringement. A dashboard saying "Brand X appeared in 37% of answers" doesn't establish whether any particular use of the mark is legally problematic. For a legal/brand-protection program, I'd want the monitoring platform to preserve the underlying responses so counsel can separately review potentially problematic uses.

If you tell me whether you're looking at this from (a) brand marketing, (b) trademark/legal compliance, or (c) an enterprise software procurement perspective, I can give you a more rigorous feature-by-feature comparison of Profound vs. Peec vs. Otterly vs. the other enterprise options.

If your goal is specifically enterprise-grade monitoring and auditing of how often a trademark/brand appears in conversational-AI answers, the current market is best thought of as AI visibility / answer-engine monitoring rather than traditional trademark monitoring.

For an enterprise team, I’d put these on the shortlist:

Current comparisons consistently place Profound toward the enterprise/deeper-analytics end, while Peec and Otterly are generally positioned toward mid-market or more accessible deployments. Scrunch is another enterprise-oriented option with broad engine coverage. baarely.com

The important distinction for trademark auditing

I'd make sure the software can distinguish at least these four things:

1. Mention — the AI actually names your trademark.
2. Citation — the AI links to your site or another source associated with you.
3. Context — what the AI says about the trademark: product, category, competitor comparison, sentiment, etc.
4. Frequency / coverage — percentage of a controlled prompt set where the trademark appears, over time.

That's important because "AI visibility" isn't necessarily the same thing as trademark exposure. A brand can have high visibility but be mentioned only in certain contexts, while another brand might receive fewer mentions but appear in much more commercially important answers. Some tools also combine mentions and citations into a single visibility metric, which can obscure the distinction. www.austinheaton.com

If this is for legal / brand-protection rather than marketing

I'd evaluate the vendors somewhat differently. Ask whether they can:

• preserve the actual AI response as audit evidence;
• timestamp every observation;
• retain the exact prompt and model/engine used;
• monitor multiple geographic markets and languages;
• detect misspellings, confusingly similar names, and unauthorized uses of the trademark;
• distinguish your trademark from similarly named entities;
• export raw observations/API data;
• provide RBAC, SSO, audit logs and enterprise data controls;
• alert legal/brand teams when a material change occurs;
• show the source/citation that influenced the answer;
• reproduce or substantiate a finding rather than merely giving you a proprietary "visibility score."

That last point is particularly important if the output may ultimately be used by legal, compliance, or brand-protection teams rather than just marketing.

For a large enterprise, I'd start with Profound and Scrunch, then evaluate Peec if you want a somewhat lighter deployment. That's not a ranking of product quality; it's based on the different enterprise/monitoring profiles reported in current comparisons. baarely.com

If you tell me whether you're thinking about legal trademark enforcement, brand monitoring, or marketing/SEO, I can give you a much more specific enterprise shortlist and a feature-by-feature RFP scorecard (including API, audit trail, model coverage, prompt volume, SSO/SOC 2, data retention, and estimated cost).

Yes. What you’re looking for is increasingly called AI search visibility / AEO / LLMO tracking: tools that put traditional Google rankings alongside how often your brand gets mentioned or cited in ChatGPT, Gemini, Perplexity, and Google’s AI results.

The closest matches

• semrush.com — probably the closest to what you described. Semrush can track Google keyword rankings and AI visibility in the same ecosystem, including ChatGPT, Gemini, Perplexity, Google AI Mode, and AI Overviews. You can define keywords/prompts and monitor them over time. Semrush Semrush
• ahrefs.com — particularly strong for comparing SEO/search demand with AI mentions and citations. It tracks ChatGPT, Gemini, Perplexity, Copilot, Google AI Overviews and AI Mode, and lets you compare your AI share of voice against competitors. Ahrefs Help Center Ahrefs
• ahrefs.com — useful if you just want to test the concept before paying. It reports AI mentions, cited pages/domains, and the platforms where you're appearing. Ahrefs

The important distinction

Traditional SEO gives you something like:

“For best accounting software, my page ranks #3 on Google.”

AI-search tracking gives you:

“For what accounting software should a small business use?, ChatGPT mentions my company in 18% of tracked answers, cites my site in 7%, and competitor X appears in 42%.”

Those aren't directly equivalent rankings. AI answers don't consistently have a numbered #1–#10 ranking. The more useful metrics are mention rate, citation rate, share of voice, position/prominence in the answer, and which URLs are being cited. Ahrefs, for example, explicitly separates mentions from citations and also tracks pages that AI found but didn't cite. Ahrefs Help Center

If your goal is specifically “SEO vs AI visibility”

I'd structure the dashboard like this:

Semrush is probably the first one I'd investigate if you want the same keyword/prompt universe and a unified SEO + AI dashboard. Ahrefs is especially interesting if you want to understand why particular pages/domains are getting cited by AI. Semrush Ahrefs

If you tell me what kind of site/business you have and roughly how many keywords you track, I can also compare the current tools (Semrush, Ahrefs, Profound, Otterly, Peec, etc.) specifically for “Google rankings vs ChatGPT rankings” and tell you what each one actually measures.

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 “AI assistants are saying inaccurate or negative things about our brand, and we need to detect and investigate those answers”, I’d evaluate the market a little differently from ordinary “AI visibility” tools.

The main options I’d shortlist are:

For negative or inaccurate mentions, I'd prioritize these capabilities

Don't buy based primarily on a generic “AI visibility score.” You want the tool to preserve the underlying answer so your team can determine why something is wrong.

1. Exact answer capture — the actual ChatGPT/Claude/Gemini response, not just “brand sentiment = negative.”
2. Prompt-level monitoring — e.g. track questions such as “Is [Brand] legitimate?”, “What are the problems with [Brand]?”, “[Brand] alternatives”, etc.
3. Factual-accuracy detection — ability to flag claims such as wrong pricing, discontinued products, incorrect executives, outdated policies, fabricated controversies, etc.
4. Source/citation tracing — identify what websites or documents appear to be causing the incorrect claim.
5. Sentiment/context — distinguish genuinely negative statements from neutral mentions of a negative topic.
6. Change alerts — notify you when an answer changes materially.
7. Geography/model variation — the same prompt can produce different answers depending on model, location and time.
8. Historical snapshots — essential if you're trying to demonstrate that a particular false claim appeared and subsequently disappeared.
9. Workflow/API — send serious incidents into Slack, Jira, ServiceNow, your PR workflow, etc.

Otterly, for example, explicitly says it stores generated answers and tracks the brand's mention, position, tone, competitors and cited URLs across seven AI engines. otterly.ai

My practical recommendation

If you're evaluating vendors today, I'd run a pilot with Otterly + Peec + Profound, using your own 50–100 high-risk prompts rather than their demos.

The key test isn't “which dashboard looks nicest?” It's:

Can the system reliably find a materially false or damaging statement about our brand, show me the exact answer, identify the source behind it, and alert the right person when it changes?

That's a substantially narrower requirement than generic GEO/AEO monitoring. Current comparisons show that these platforms overlap heavily on basic mention/citation tracking but differ considerably in depth, engine coverage and enterprise functionality. www.austinheaton.comgeohats.com

If you tell me your company size, approximate number of brands/products, which AI systems matter (ChatGPT only vs. ChatGPT + Claude + Gemini + Perplexity), and whether you need automated alerts, I can make you a vendor-by-vendor comparison with pricing, accuracy monitoring, APIs, alerting, and implementation effort.

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