Answers that name a competitor

Yes. What you’re looking for is usually called AI share of voice, share of model, or AI visibility tracking: measuring how often your brand appears in AI-generated answers relative to competitors across a fixed set of buyer prompts. Geo Software Rankings Verity Score

Tools I’d shortlist

• tryprofound.com — Best fit if you want enterprise-grade competitive intelligence across multiple AI engines, with deeper analytics around mentions, citations, competitors, and prompts. It’s commonly positioned as an enterprise leader. AnswerManiac Refine AI
• peec.ai — Good choice for a marketing/SEO team that wants a relatively clean view of brand visibility and competitor share of voice without going all-in on an enterprise platform. Sapian Metrics Omid Saffari
• otterly.ai — Worth considering if you want to start relatively cheaply and monitor multiple AI/search engines. AnswerManiac Refine AI
• citedspy.com — Particularly interesting for a quick/free competitive snapshot. Its checker reports visibility, competitor share of voice, prompt-level rankings, sentiment, and cited sources across ChatGPT, Perplexity, Gemini, Claude, and Grok. CitedSpy
• modelmention.io — Another straightforward option focused specifically on visibility, position, share of voice, sentiment, and citations, with tracking across ChatGPT, Gemini, and Perplexity. ModelMention

If your primary question is literally “What % of AI answers mention us vs. Competitor A/B/C?”, I'd start with Peec or Profound. If you just want to validate the concept before buying anything, try CitedSpy's free checker. AnswerManiac CitedSpy

One important caveat: don't rely on a single ChatGPT query. AI answers are nondeterministic, and different engines can surface substantially different brands and sources. A meaningful measurement should use many buyer prompts, repeated runs, multiple engines, and a consistent competitor set/time window. Verity Score foglift.io

If you tell me your brand + 3–5 competitors + industry, I can also recommend which of these tools is the best fit and what metrics I'd track.

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

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

Some tools to evaluate:

A typical measurement setup looks like:

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

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

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

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

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

Yes. What you’re looking for is usually called AI visibility tracking, GEO (Generative Engine Optimization), or AEO tracking. These platforms repeatedly run the questions your buyers ask through AI engines and measure mention rate, share of voice, competitor presence, ranking/position, sentiment, and citations. PickMySoft AI Visibility Snapshot

My shortlist

Recent comparisons consistently put Profound, Peec, Otterly and Scrunch among the leading dedicated options. Omid Saffari Bloomiro WikiAP

What I'd pick

If your primary question is literally "What percentage of AI answers mention us vs. our competitors?" → Peec AI or Profound.

I'd lean Peec if you're a mid-sized marketing/product team and want a clean competitive dashboard without going all-in on an enterprise platform. I'd lean Profound if this is becoming a strategic measurement program across many products, markets, prompts and AI engines. Current comparisons characterize Peec as the mid-market/competitive-analysis option and Profound as the enterprise-depth option. Bloomiro Stork.AI

If you're trying to spend as little as possible → Otterly.AI. It has a much lower entry point and is designed around scheduled prompt monitoring, although you'll want to check the cost of additional engines as your requirements expand. 1001 SEO Media Stork.AI

The metrics I would insist on

Don't buy a tool just because it gives you an "AI visibility score." You want to be able to see:

• Mention rate: You appeared in 38% of relevant AI responses; Competitor A appeared in 61%.
• Share of voice: Your proportion of all brand mentions within the competitive set.
• Position: Whether you're the #1 recommendation, #3, etc.
• Prompt-level results: Which exact customer questions cause competitors to beat you.
• Engine breakdown: ChatGPT vs. Gemini vs. Perplexity vs. Claude vs. Google AI experiences.
• Citation/source gap: Which websites AI systems cite when recommending your rivals but don't cite you.
• Trend over time: Whether your share is actually increasing.
• Sentiment/context: Whether the AI mentions you positively, neutrally, or negatively.

That distinction matters because a raw mention count can be misleading; the useful competitive metric is share of voice across a consistent set of buyer prompts, with citation analysis explaining why you're winning or losing. AI Visibility Software AI Visibility Software

If you tell me roughly how many products/competitors you want to track and your monthly budget (e.g. <$100, $100–500, $500+), I can narrow this to the 2–3 best choices and compare their current pricing, AI engines, competitor tracking, and reporting capabilities.

If your primary question is literally "What percentage of AI answers mention us vs. our competitors?" → Peec AI or Profound.

I'd lean Peec if you're a mid-sized marketing/product team and want a clean competitive dashboard without going all-in on an enterprise platform. I'd lean Profound if this is becoming a strategic measurement program across many products, markets, prompts and AI engines. Current comparisons characterize Peec as the mid-market/competitive-analysis option and Profound as the enterprise-depth option.

If you're trying to spend as little as possible → Otterly.AI. It has a much lower entry point and is designed around scheduled prompt monitoring, although you'll want to check the cost of additional engines as your requirements expand.

The metrics I would insist on

If your goal is specifically “How often does AI recommend us vs. competitors?”, you’re looking for an AI search visibility / GEO monitoring platform. These tools repeatedly run buyer-intent prompts and measure mentions, position, share of voice, sentiment, and citations. AI Visibility Snapshot KITLabs

My shortlist

What I'd pick

For a B2B product where competitive visibility is the primary KPI, I'd start with Peec AI or Profound.

The dashboard you ultimately want should look something like:

Buyer prompt: “Best customer-support platforms for a 50-person SaaS company?”
ChatGPT: You #2 · Competitor A #1 · Competitor B #4
Perplexity: You #1 · Competitor B #2
Gemini: You absent · Competitor A #1
Overall AI share of voice: You 31% vs A 42% vs B 18%
Citation share: You 24% vs A 39%
Trend: You +7 pts over 30 days

That's much more useful than simply counting whether your company name appears.

One important caveat: don't judge a tool by its aggregate “AI visibility score.” AI responses vary substantially by prompt and engine; recent testing found that the same brands were named across all three of ChatGPT, Perplexity and Gemini only about 21% of the time. Reddit You want the underlying prompt × model × competitor data.

If you tell me your product category, roughly how many competitors you want to track, and your monthly budget, I can narrow this to the best 2–3 options and compare their current pricing/features.

Yes. There’s now a fairly mature category of tools for this, usually called AI visibility / GEO (Generative Engine Optimization) platforms. They track how often your brand appears in AI answers relative to competitors and, importantly, show the change over time.

The strongest options I’d look at

• semrush.com — Probably the most established choice if you already use Semrush. Its AI share of voice can be tracked against competitors over a selected time period, with platform-level breakdowns. Semrush
• siftly.ai — Particularly focused on the exact metric you're describing: overall, platform-specific, topic-specific, and historical SOV, with daily/weekly/monthly trends. Siftly
• presenc.ai — Strong emphasis on historical analysis, including weekly/monthly/quarterly changes across ChatGPT, Perplexity, Claude and Gemini. Presenc AI
• rankscope.ai — Tracks SOV, mention rate, ranking position, sentiment and citation sources across major AI engines. RankScope
• seerly.app — Good if you want to see an actual competitive SOV trend, broken down by AI engine and prompt. Seerly
• signal360.ai — More enterprise-oriented, with multi-LLM analysis and SOV tracking across days, weeks and months. Signal 360

There's also a useful distinction between your own brand tracking and industry/category benchmarking. For example, helloranked.com publishes a broader AI-visibility leaderboard by industry, including monthly share-of-voice measurements across ChatGPT, Claude, Gemini and Perplexity. helloranked

What I'd want the platform to show

If your goal is "How is AI's perception of my industry changing?", rather than just "How visible is my company?", I'd prioritize a platform that lets you:

1. Define a set of industry/buyer prompts — e.g. "best accounting software for startups," "top cybersecurity vendors," etc.
2. Track every major AI engine separately.
3. See brand SOV vs. competitors over time.
4. Segment SOV by topic / intent / use case, not just one aggregate number.
5. See the actual AI responses and citations behind changes.
6. Ideally compare your industry against broader AI-search trends.

That's important because an overall "AI visibility score" can hide what's actually happening. A brand might be gaining visibility overall while losing badly on the high-intent prompts that matter commercially.

If you tell me your industry (and ideally your company/competitors), I can narrow this down to the 2–3 platforms that actually have the best data for your particular market, including which ones show historical industry benchmarks rather than just your own tracking.

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

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

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

The important distinction

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

1. Your company's AI SOV

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

That's what the platforms above are primarily measuring.

2. Industry-wide AI SOV trends

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

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

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

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

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

Yes. I’d treat this as an AI-answer Share of Voice (SoV) monitoring problem rather than traditional SEO rank tracking.

My recommendation: start with Profound

tryprofound.com is probably the strongest fit if your goal is specifically “How often does ChatGPT mention us vs. competitors, and how is that changing?”

It tracks prompts across ChatGPT, Gemini, Perplexity, Google AI Overviews/AI Mode and other answer engines, and provides visibility, Share of Voice, competitor rankings, citations, sentiment, position, and prompt-level analysis. Profound Profound

The key metrics I'd put on an executive dashboard are:

Profound explicitly defines SoV around the frequency of your brand mentions relative to competitors and provides rankings/charts for it. Profound Knowledge Base Profound Knowledge Base

A simple way I'd calculate the headline number

Suppose you run 1,000 relevant prompts and get:

• Your company: 240 mentions
• Competitor A: 300
• Competitor B: 180
• Competitor C: 120
• Others: 160

Your AI Share of Voice = 240 / 1,000 = 24% under a mention-based methodology.

I'd actually maintain two separate metrics, though:

Visibility:
answers mentioning you / total answers

Competitive SoV:
your mentions / all tracked brand mentions

That's important because a brand can have high visibility while still losing competitive share. Peec's documentation makes the same distinction between visibility and SoV. Peec.ai Docs

Profound vs. Peec AI

peec.ai is the other product I'd seriously evaluate. It has particularly clear dashboards for competitor visibility, SoV, position and sentiment, with filtering by model, country, topic and prompt. Peec AI Peec.ai Docs

I'd choose:

• Profound → best if this becomes a serious enterprise competitive-intelligence/AEO program, especially if you care about citations and discovering unexpected competitors.
• Peec AI → attractive if you primarily want a straightforward brand vs. competitor visibility/SoV dashboard.
• Build your own → worthwhile if you have unusual prompt taxonomies, need proprietary scoring, or want to integrate the data into an existing BI/marketing stack.

The dashboard I'd build

Then drill down:

1. Overall SoV — current % and week/month change.
2. SoV by AI engine — ChatGPT vs Gemini vs Perplexity, etc.
3. SoV by topic — product comparisons, “best X,” alternatives, pricing, use cases, etc.
4. Prompt-level battles — “Competitor A appears 78%, we appear 21%.”
5. Citation gap — sites/pages cited for competitors but not you.
6. Trend — SoV over the last 30/90 days.
7. Answer quality — position + sentiment, not just presence.

The last two are important: a 25% SoV isn't necessarily better than 20% if the competitor is consistently recommended #1 and you're mentioned as an afterthought.

One important implementation detail

Don't just track a handful of manually invented prompts. Build a representative prompt universe covering your category, buying funnel, use cases, competitors, alternatives and high-intent questions, then run the same corpus repeatedly. Profound runs tracked prompts daily, while Peec also supports prompt-level and model-level analysis. Peec.ai Docs Profound

If this is for a marketing/brand team, my short answer is: pilot Profound and Peec side-by-side for 2–4 weeks, using the same 100–500 prompts, and compare the resulting SoV numbers before committing. The methodology matters as much as the dashboard.

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

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

Recommended solution

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

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

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

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

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

The metric I'd use

For every fixed prompt set, calculate:

Brand SOV = brand mentions ÷ total tracked-brand mentions

For example:

I'd actually maintain two related metrics:

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

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

How I'd implement the monitoring program

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

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

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

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

The dashboard I'd want

Then drill down to:

"Where are we losing?"

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

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

My recommendation

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

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

My recommendation

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

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

If you mean tracking how often your brand appears in AI assistant answers (ChatGPT, Gemini, Claude, Perplexity, voice assistants) versus competitors, you’re looking for an AI visibility / Generative Engine Optimization (GEO) / Answer Engine Optimization (AEO) monitoring tool. These tools usually work by running a fixed set of prompts, capturing AI outputs, and calculating metrics such as mention rate, citation rate, position, sentiment, and share of voice. Corbelix Visibility.so

Some of the main options:

For voice channels specifically (Alexa, Siri, Google Assistant-style experiences), measurement is harder because many assistants do not expose their answers in a crawlable way. Most commercial platforms today focus on AI search/chat outputs rather than true voice assistant conversations. Locafy A practical approach is usually:

1. Define a prompt set (e.g., 500 customer questions).
2. Run those prompts across target assistants.
3. Record: - brand mentioned?
4. competitor mentioned?
5. rank/order of recommendations
6. citations/sources used
7. sentiment
8. Calculate:

AI Share of Voice = your brand mentions (or citations) ÷ total brand mentions across tracked answers

SearchDock Geo Software RankingsIf you are building this internally, the stack is usually:

• API access where available (for example, model APIs)
• browser automation for assistants without APIs
• prompt orchestration
• output storage
• entity extraction/classification
• SOV dashboarding

For a large brand, I’d shortlist Profound, Otterly, Semrush, and SearchDock; for a leaner marketing team, ModelMention or Visibility.so are closer to a ready-made dashboard. Foglift Visibility.so ModelMention

Yes — this is now a defined category usually called AI visibility / AI search monitoring / GEO (Generative Engine Optimization). The tools actually run a fixed prompt set against AI assistants, capture the returned answers, identify brand/competitor mentions and citations, and calculate share of voice. Spawned Viali AI

My shortlist

• tryprofound.com — Strong enterprise-oriented choice for tracking brand presence across AI engines and analyzing what models say about you and competitors. A recent benchmark found Profound among the most frequently surfaced AI-visibility tools. Foglift
• otterly.ai — Purpose-built AI-search monitoring; particularly worth considering if your main KPI is brand mentions/share of voice across prompts and competitors.
• ahrefs.com — A good option if you're already an Ahrefs customer. It tracks ChatGPT, Gemini, Perplexity, Copilot and Google AI surfaces, including mentions, competitors and cited sources. Ahrefs
• peec.ai — Focused specifically on AI-search visibility and competitive monitoring.
• getlatentline.com — Interesting if you want answer-level evidence behind the SOV number. It tracks the prompts, answers, mentions, competitors and sources rather than just giving you a black-box visibility score. Latentline
• agentgeo.org — More API/developer-oriented. Particularly compelling if you want to build your own SOV calculation from the raw answers rather than rely on a vendor's metric. AgentGEO

One important distinction

If by "voice and chat channels" you mean spoken assistants such as Siri/Alexa/Google Assistant in addition to ChatGPT/Gemini/Perplexity, that's a different requirement.

Most of the current AI-SOV platforms primarily monitor text-based AI answers/search surfaces. For example, Ahrefs currently covers ChatGPT, Gemini, Perplexity, Copilot and Google's AI surfaces. Ahrefs

So if your goal is something like:

"Across every place a consumer can ask an AI about our category, what % of recommendations/mentions are us vs. competitors?"

I'd evaluate vendors specifically on actual answer crawling/API access, voice-assistant coverage, prompt sampling, repeat runs, competitor detection, and whether you can export the raw answers. Because AI answers are nondeterministic, multiple runs per prompt are important for a defensible SOV number. verityscore.io organikpi.com

If you tell me your industry and whether you mean ChatGPT/Gemini/Perplexity only or also Siri/Alexa/voice assistants, I can narrow this to the 2–3 tools I'd actually shortlist.

If you’re a small ecommerce store and genuinely budget-constrained, I’d start with otterly.ai.

My ranking

1. Otterly.ai — best budget pick

• Starts at $29/month for 15 tracked prompts. CiteDaily AEO Labs
• Tracks mentions/citations and visibility across ChatGPT, Google AI Overviews, Perplexity and Copilot. CiteDaily
• Good for answering the basic question: “Are AI engines recommending/citing my store, and who are they recommending instead?”
• The downside: Gemini and Google AI Mode are paid add-ons, and the jump from 15 to 100 prompts is substantial. CiteDaily AEO Labs
• peec.ai — better if you can spend ~$95/mo

• Starter is $95/month for 50 prompts, 3 models and daily tracking. Peec AI
• Stronger reporting around share of voice, position, sentiment and the sources AI cites. Peec AI
• I'd choose this over Otterly if AI search is already generating meaningful sales for you and you want to optimize systematically.
• tryprofound.com — overkill for your situation

• Much more geared toward larger organizations and sophisticated AI-search programs. Current comparisons put its useful tiers well above what I'd recommend for a small store. Loudmink SEO Counselors

What I'd actually do

For a tight-budget ecommerce store, don't try to track hundreds of prompts. Start with ~15 highly commercial questions, such as:

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

Track your store plus 3–5 competitors.

Then watch three numbers:

AI citation share → brand mention rate → position/recommendation frequency

The important distinction is that being mentioned isn't the same as being cited as a source. For ecommerce, I would particularly care about which third-party sites AI is using to form its recommendation—because those are often the sites you need to get your products/brand mentioned on.

Bottom line:

• <$50/mo: Otterly
• ~$100/mo and want better citation intelligence: Peec
• >$300/mo: only consider the heavier platforms if AI search is already a significant acquisition channel.

If you tell me your monthly budget (e.g. $30, $50, $100) and what you sell, I can narrow this down to the best option and tell you exactly what prompts I'd track.

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

My ranking

1. OtterlyAI — best budget pick

• Entry plan is around $29/month for a small set of tracked prompts. AEO Labs Loudmink
• Tracks share of voice, mentions, competitors, sentiment, and the actual URLs AI engines cite. Otterly
• Covers ChatGPT, Google AI Overviews, Perplexity and Copilot on the entry tier; additional engines can cost extra. AEO Labs
• Particularly relevant for ecommerce because it now has AI Shopping Analytics, including how products surface and which retailers appear alongside you. Otterly
• Downside: 15 prompts isn't much, so you'll need to be selective about the questions you monitor.
• peec.ai — better if you can spend ~$90–100/month

• More attractive once you need a larger prompt set and deeper citation/source analysis.
• Several 2026 comparisons put it around $95/month at entry, with more substantial multi-engine monitoring. AEO Labs AEO Labs
• I'd choose this over Otterly if AI search is already producing meaningful revenue for you.
• tryprofound.com — overkill for you

• Excellent enterprise-level visibility/citation intelligence, but the economics don't make much sense for a small store. Loudmink SEO Counselors

What I'd actually track

Don't waste your 15 Otterly prompts on generic searches like "best running shoes." Build prompts around commercial intent and product discovery, e.g.:

• "Best [product category] for [use case]"
• "[Product category] brands recommended for [customer type]"
• "What are the best alternatives to [competitor]?"
• "Where can I buy [product type]?"
• "[Your brand] vs [competitor]"
• "Best [product category] under $X"
• "What should I look for when buying [product]?"

Then watch three numbers:

AI citation share → competitor citation share → which URLs are being cited.

The third one is arguably the most actionable. If ChatGPT consistently cites a competitor's buying guide while ignoring your product/category page, you've learned where your content/authority gap is—not merely that you have an AI visibility problem. Otterly explicitly reports the cited URLs and competitor citations. Otterly

Bottom line: If your budget ceiling is ~$30/month, pick Otterly. If you can justify ~$100/month and want more serious citation/share-of-voice analysis, pick Peec. I wouldn't spend enterprise money on Profound for a small ecommerce operation.

If you tell me what you sell + your monthly AI/SEO budget, I can also tell you exactly which 15 prompts I'd put into Otterly to get the most useful citation-share baseline.

Yes. There’s now a fairly mature category usually called AI search visibility, AI visibility, AEO (answer engine optimization), or GEO (generative engine optimization).

For an enterprise team specifically looking for “how visible are we in AI answers, and how do we compare with competitors?”, I’d shortlist:

• tryprofound.com — probably the strongest fit for enterprise. It tracks brand mentions/citations across major AI engines and is particularly focused on competitive benchmarking, prompt-level analysis, and enterprise reporting. Recent comparisons consistently position it as the enterprise/deep-analytics option. AirOps Refine AI
• scrunch.com — enterprise AI visibility monitoring plus an “Agent Experience Platform” that can help make site content more accessible/usable to AI agents. It also supports competitive monitoring. HubSpot Blog Scrunch
• peec.ai — more analytics/monitoring oriented and generally positioned as a good mid-market or multi-brand option. Stronger if your primary need is benchmarking rather than enterprise infrastructure. WikiAP
• semrush.com — worth considering if you're already heavily invested in Semrush and want AI visibility alongside traditional SEO data. NBound Marketing
• ahrefs.com — another option if your organization already uses Ahrefs; it brings AI-answer visibility into the broader SEO/brand-monitoring workflow. NBound Marketing

What these platforms actually benchmark

The useful ones go beyond simply asking “Does ChatGPT mention my company?” They can measure things like:

Competitive benchmarking is explicitly becoming a core feature of these platforms rather than an afterthought. Scrunch Fulcru

If you're evaluating this as an enterprise buyer

I'd put Profound, Scrunch, and Peec into the first RFP/demo round, then evaluate them on something more specific than “number of prompts.”

The important questions are:

1. Can we upload our actual customer questions?
2. Can we track the same prompt set against 5–20 competitors?
3. Can we segment by product, market, geography, persona, and funnel stage?
4. Which AI engines/models are actually measured, and how frequently?
5. Do we get the underlying answer + citations, not just a proprietary visibility score?
6. Can we export the data/API it into our BI/data warehouse?
7. Can we distinguish organic AI visibility from paid/sponsored results?
8. Can we see why a competitor is winning the answer—e.g. which sources AI is citing?
9. Can multiple brands/business units be managed in one enterprise workspace?
10. Is the measurement methodology stable enough that executives can use the score as a KPI?

That last one is particularly important: “AI visibility score” isn't yet a standardized metric. Different vendors can run different prompts, frequencies, models, and sampling methodologies, so two vendors' percentages aren't necessarily comparable. Citlyze

If you're asking because you're considering building/buying something in this space, I can also map out the enterprise competitive landscape—who has the best data, benchmarking, integrations, pricing, and white-space opportunities.

Yes. There’s now a fairly established AI visibility / AEO / GEO software category that does exactly this: tracks whether your brand appears in AI-generated answers and benchmarks you against competitors.

For an enterprise team, I’d look at these first:

• tryprofound.com — probably the clearest pure-play enterprise option. It tracks brand visibility across AI answer engines, prompt-level performance, citations, and competitor share of voice. It’s repeatedly positioned as the enterprise-oriented choice. Mention Radar AEO Labs
• similarweb.com — interesting if you want AI visibility connected to traffic, market intelligence, and competitive benchmarking, rather than just an AEO dashboard. Recent comparisons highlight its ability to connect AI visibility with broader digital/traffic context. Prism News Prism News
• peec.ai — more focused on AI-search monitoring and competitive visibility. Good fit if the core question is “how often are we recommended vs. competitors, and in which prompts/models?” Conbersa Mention Radar
• athenahq.ai — worth evaluating if you want the visibility data connected to GEO/content workflows and reporting rather than just measurement. Prism News
• sona.com — particularly interesting because it says it runs prompts against the consumer-facing AI experiences, then captures mentions, position, sentiment, citations, and competitor benchmarks. Sona

What these platforms actually measure

The useful ones go beyond a generic “AI visibility score.” They can give you things like:

That competitor + prompt + citation-source layer is the part I'd pay particular attention to. A single AI visibility score can be pretty vanity-oriented; knowing “Competitor X is winning 37 high-intent prompts because these 14 third-party pages are being cited” is much more actionable. AI Visibility Software Prism News

If you're evaluating this as a potential enterprise software category / product opportunity, there's an interesting distinction: most of these tools are essentially “rank trackers for AI answers.” The more differentiated products are moving toward competitive intelligence + attribution + recommendations/workflows. For example, newer platforms explicitly position themselves around connecting the visibility gap to the sources and actions needed to close it. geovium.com franixo.com

If you tell me whether you're shopping for a tool or thinking about building one, I can map the competitive landscape and tell you where the gaps are.

Yes. If your goal is specifically “we changed a piece of content → did our AI share of voice/citation share improve?”, I’d look at dedicated AI-visibility/GEO platforms rather than traditional SEO rank trackers.

My shortlist

What I'd choose

For your exact use case, I'd start with Peec or Profound.

The important capability isn't simply tracking whether your brand is mentioned. You want the tool to preserve a stable prompt set and let you compare:

Pre-update: Brand SOV = 12%
Content update: revised comparison page
Post-update: Brand SOV = 21%
Change: +9 percentage points

Ideally, you also want citation share, competitor share, answer position, sentiment, and which URLs the AI cited.

That's important because AI visibility can move substantially even without changes to your site. Recent data, for example, showed Reddit's share of ChatGPT citations falling sharply over a few weeks—illustrating how volatile these measurements can be. Axios

One thing I'd insist on

Don't evaluate these tools based on a single “AI visibility score.” Set up an experiment framework:

1. Freeze 50–200 representative buyer prompts.
2. Record baseline SOV/citation share for 2–4 weeks.
3. Update specific pages.
4. Keep the prompt set unchanged.
5. Continue measuring for several weeks.
6. Compare treated pages/topics vs. untouched pages/topics where possible.
7. Look at citations, not just mentions.

That gives you a much better answer to “did our content change cause AI visibility to improve?” rather than merely “did our AI score go up?”

If you tell me your company size, approximate monthly budget, and whether you're optimizing for ChatGPT only or ChatGPT + Google AI Overviews + Perplexity, I can narrow this to the best 2–3 options and compare their measurement capabilities.

The important capability isn't simply tracking whether your brand is mentioned. You want the tool to preserve a stable prompt set and let you compare:

Pre-update: Brand SOV = 12%
Content update: revised comparison page
Post-update: Brand SOV = 21%
Change: +9 percentage points

Ideally, you also want citation share, competitor share, answer position, sentiment, and which URLs the AI cited.

That's important because AI visibility can move substantially even without changes to your site. Recent data, for example, showed Reddit's share of ChatGPT citations falling sharply over a few weeks—illustrating how volatile these measurements can be.

One thing I'd insist on

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

Here are the strongest options:

My shortlist by use case

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

Metrics I would require before buying

Make sure the tool tracks:

1. Prompt-level SOV

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

2. Citation changes

Did your updated article become a cited source?

3. Competitor movement

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

4. Pre/post content versioning

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

5. Engine breakdowns

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

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

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

The category you’re looking for is usually called AI visibility tracking, Generative Engine Optimization (GEO) analytics, or Answer Engine Optimization (AEO) measurement. These tools run buyer-style prompts through multiple AI assistants and quantify things like brand mentions, citations, ranking/recommendation frequency, sentiment, and share of voice. AI Visibility Snapshot SynectGEO

Some options:

• Ahrefs — its AI Visibility Checker / Brand Radar tracks how brands appear across assistants such as ChatGPT, Gemini, Perplexity, Copilot, and Google AI Overviews, with metrics like mentions, citations, competitor comparisons, and cited pages. Ahrefs
• Profound — built specifically for monitoring brand presence in AI answers and measuring how often assistants recommend or reference brands. OTW Motion
• ViAudit — runs prompts across ChatGPT, Perplexity, Gemini, Claude, Copilot, Google AI Overviews, and Grok and provides a visibility score and recommendations. Viaudit
• Geopsy — focuses on tracking brand mentions and visibility scores across ChatGPT, Gemini, Claude, and Perplexity. geopsy
• SynectGEO — measures visibility, citations, competitors, sentiment, and cited sources across major AI assistants. SynectGEO
• Orbilo — emphasizes unified visibility scores, share-of-voice benchmarking, and prompt tracking across assistants. Orbilo

When evaluating one, look for these metrics:

• Mention rate: % of relevant AI answers where your brand appears.
• Recommendation rate: how often the assistant recommends you versus competitors.
• Share of voice: your share of brand mentions compared with alternatives.
• Citation rate: how often your website or trusted sources are cited.
• Position/prominence: whether you are the first recommendation, in a shortlist, or a passing mention.
• Sentiment/context: whether the assistant describes your brand positively, neutrally, or negatively.
• Prompt coverage: whether it tests the actual questions your customers ask.

For an enterprise brand, I’d generally prioritize a platform that lets you upload your own prompt set (e.g., “best CRM for mid-market companies,” “alternatives to [competitor]”), track competitors, and retain historical trends—not just a one-time score. foglift.io Orbilo

Yes. The category you’re looking for is usually called AI visibility / answer-engine visibility / GEO monitoring.

Best fits

• tryprofound.com — probably the strongest fit if you want an enterprise-grade measurement system. It queries the actual consumer-facing experiences of ChatGPT, Claude, Gemini, Perplexity, Copilot, Google AI Overviews/AI Mode, Grok, and DeepSeek, then measures visibility, share of voice, sentiment, citations, positioning, and competitor performance. Profound Profound
• otterly.ai — a strong, more accessible option. It tracks prompts across ChatGPT, Perplexity, Gemini, Claude, Copilot, and Google's AI experiences, with brand coverage, share of voice, rank, sentiment, and citation tracking. Otterly Otterly
• ahrefs.com — useful for a quick/free snapshot of how a brand appears across ChatGPT, Gemini, Perplexity, Copilot, and Google AI Overviews. It's better for checking visibility than for a sophisticated ongoing conversational-AI measurement program. Ahrefs

What I'd measure

For a serious brand program, I wouldn't use just "number of mentions." I'd want:

AI Visibility =

• % of relevant prompts where you're mentioned
• Share of voice vs. competitors
• Position/rank within the answer
• Recommendation vs. neutral mention vs. negative mention
• Citation frequency and which domains AI trusts
• Visibility by assistant (ChatGPT vs. Gemini vs. Claude, etc.)
• Visibility by topic, customer intent, geography, and prompt
• Change over time

Profound and OtterlyAI both explicitly support this kind of measurement. Profound Knowledge Base Otterly

If you're asking "which one should we buy?" I'd start with Profound for an enterprise brand and OtterlyAI for a leaner marketing team. The important distinction is that these tools don't merely count web mentions—they actually run representative conversational prompts against multiple AI answer engines and analyze the resulting answers. Profound Knowledge Base help.otterly.ai

If you tell me your brand/category and whether you're B2B or B2C, I can also compare Profound vs. OtterlyAI vs. Ahrefs specifically for your use case.

For long-tail / niche SaaS queries, I’d shortlist these:

1. peec.ai — probably the best fit. It tracks Share of Voice at the individual-prompt level, lets you build a prompt library, compares competitors, and tracks visibility/position across AI models. Its docs also mention prompt-level search-volume estimates and query-fanout data, which is particularly useful when your keywords are niche rather than high-volume head terms. Peec AI Peec.ai Docs GlobeNewswire
2. otterly.ai — strong alternative. You can manually add the exact natural-language prompts you care about and monitor them daily. It reports brand coverage, mentions, competitors, citations, and SoV, with breakdowns by prompt and engine. Otterly OtterlyAI

My pick for your use case

If you're trying to answer something like:

“For 200 highly specific SaaS buyer queries, what % of AI answers mention us vs. G2, Capterra, Competitor X, etc.?”

I'd start with Peec AI. Its prompt-centric model and per-prompt SoV are closer to measuring long-tail AI query share of voice than traditional SEO rank trackers. Peec AI Peec.ai Docs

One important distinction: AI SoV isn't the same as Google keyword SoV. These tools generally track the exact prompts you submit and the resulting AI answers, so for niche SaaS you want a tool that lets you maintain a sufficiently large custom prompt set rather than relying primarily on estimated search volume.

If you tell me roughly how many long-tail queries (e.g. 100, 1,000, 10,000) and which AI surfaces you care about (ChatGPT, Google AI Overviews, Perplexity, Claude, etc.), I can narrow it down to the best tool and plan.

If you mean “track a defined set of niche SaaS buyer queries and tell me what % of AI answers mention my product vs competitors”, the strongest fits right now are:

• otterly.ai — probably the best starting point for a niche SaaS. It explicitly tracks share of voice by prompt, AI engine, and market, plus competitor mentions and citation URLs. It can monitor ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini, Copilot, and Claude. Otterly
• peec.ai — particularly worth looking at if your focus is B2B SaaS + competitor share of voice and you want a straightforward monitoring workflow. DeepSmith
• tryprofound.com — better if you need enterprise-grade analysis, broader engine coverage, citation analysis, and deeper reporting. DeepSmith DataDab
• similarweb.com — interesting if you want to connect AI visibility/SOV with traffic and broader competitive intelligence, rather than just monitor prompts. Prism News Prism News

For your specific use case

I'd shortlist Otterly vs. Peec first.

The important distinction is that you don't really want a generic “AI visibility score.” For niche SaaS keywords, you want to be able to define a prompt universe such as:

• “best [category] software for [ICP]”
• “[competitor] alternatives”
• “tools for [specific workflow]”
• “best [category] for startups”
• “[category] software under $X”
• “[pain point] software”
• “[use case] platforms”

…and then see something like:

That's much more actionable than simply asking whether your domain is “visible.”

Otterly is the one I'd test first for this exact requirement because its documentation explicitly supports SOV broken down by prompt + engine + market, and its Gap Analyzer identifies prompts where competitors appear and you don't. Otterly

One caveat: “keyword” in AI search isn't quite the same thing as an SEO keyword. The best tools measure a prompt/query set, so if you have 100 niche SaaS keywords, I'd want a tool that lets you turn those into multiple buyer-intent prompts rather than simply checking one exact phrase.

If you tell me your SaaS category + roughly how many keywords/prompts you need to track, I can narrow this down to the best 2–3 tools and compare pricing, prompt limits, engines, SOV methodology, and API/export options.

If your priority is comparing AI share of voice across countries and languages, I’d shortlist these:

1. Profound — my first choice

Profound is probably the closest match if you need a global AI SOV operating system, rather than just an AI rank tracker.

Its Answer Engine Insights supports Share of Voice, visibility, citations and positioning, with filters for regions, platforms, prompts, topics and personas. More importantly for your question, Profound says it supports 30+ languages and 150+ regions. Profound Profound Knowledge Base

You can therefore construct something like:

Brand × Country × Language × AI engine × Topic × Competitor → AI Share of Voice

It also has a dashboard specifically designed for SOV reporting and filtering by regions. Profound

Best if: you're a multinational brand and want to answer questions such as “Why are we 31% SOV in Germany but only 8% in France?”

2. Peec AI — particularly good for country comparisons

Peec is the one I'd demo alongside Profound.

It explicitly tracks visibility, position, sentiment and Share of Voice, and lets you benchmark competitors across AI engines. Peec AI

What's particularly relevant is its country-gap workflow: Peec can break brand visibility and SOV down by country code, then identify which competitor dominates each market. Peec AI

Best if: your main question is “Which countries are we losing AI visibility in, and to whom?”

3. Semrush — best if you want AI + conventional search

If your organization already uses Semrush, I'd seriously consider consolidating there. Semrush has been moving aggressively into AI Visibility/GEO while retaining its traditional international SEO capabilities. MarketsandMarkets

The advantage isn't necessarily that its AI SOV measurement is superior to Profound/Peec; it's that you can compare:

Google organic SOV → AI SOV → traffic → competitors

within a broader search intelligence stack.

One important caveat

Don't treat "AI Share of Voice" as a standardized industry metric. Different vendors calculate it differently. For example, Profound defines SOV as your brand's mentions divided by total brand mentions across the tracked answers, while other vendors use position-weighted or impression-like methodologies. Profound Knowledge Base Verity Score

For a serious multinational measurement program, I'd therefore insist that the vendor lets you inspect:

• Exact prompts being measured
• Country/locale used when querying the model
• Language of the prompt and language of the answer
• AI engine/model
• Competitor set
• Raw answer/mention data
• SOV calculation methodology
• Sampling frequency
• Historical data
• Ability to export data/API it
• Consistent prompt sets across markets

Otherwise, an apparent "France vs Germany SOV gap" can partly be a measurement-design artifact.

My ranking for your specific requirement

If you're evaluating software today:

1. 🥇 Profound — strongest fit for enterprise, multilingual, multi-country AI SOV
2. 🥈 Peec AI — excellent for country-by-country competitor gaps and easier operational analysis
3. 🥉 Semrush — strongest if AI visibility needs to sit alongside international SEO
4. Similarweb — especially interesting if market/traffic intelligence matters as much as SOV
5. OtterlyAI — good if you want a more lightweight monitoring solution

If you tell me roughly how many countries/languages you need (e.g. 10 vs 50), your company size, and whether you care about ChatGPT/Claude/Gemini/Perplexity/Google AI Overviews, I can narrow this to 2–3 platforms and compare their actual geographic, language, prompt, API, and enterprise capabilities.

You can therefore construct something like:

Brand × Country × Language × AI engine × Topic × Competitor → AI Share of Voice

It also has a dashboard specifically designed for SOV reporting and filtering by regions. Profound

Best if: you're a multinational brand and want to answer questions such as “Why are we 31% SOV in Germany but only 8% in France?”

2. Peec AI — particularly good for country comparisons

Peec is the one I'd demo alongside Profound.

It explicitly tracks visibility, position, sentiment and Share of Voice, and lets you benchmark competitors across AI engines. Peec AI

What's particularly relevant is its country-gap workflow: Peec can break brand visibility and SOV down by country code, then identify which competitor dominates each market. Peec AI

Best if: your main question is “Which countries are we losing AI visibility in, and to whom?”

3. Semrush — best if you want AI + conventional search

If your organization already uses Semrush, I'd seriously consider consolidating there. Semrush has been moving aggressively into AI Visibility/GEO while retaining its traditional international SEO capabilities. MarketsandMarkets

The advantage isn't necessarily that its AI SOV measurement is superior to Profound/Peec; it's that you can compare:

Google organic SOV → AI SOV → traffic → competitors

within a broader search intelligence stack.

One important caveat

Don't treat "AI Share of Voice" as a standardized industry metric. Different vendors calculate it differently. For example, Profound defines SOV as your brand's mentions divided by total brand mentions across the tracked answers, while other vendors use position-weighted or impression-like methodologies.

For a serious multinational measurement program, I'd therefore insist that the vendor lets you inspect:

If your core requirement is AI share of voice by country + language + competitor, I’d shortlist these:

My top 3

1. Profound — best overall for serious global benchmarking

Profound is probably where I'd start if you're evaluating this for a multinational brand. Its current offering emphasizes cross-market brand visibility, and its 2026 index is based on 1.9B+ real user conversations across 50+ industries and major answer engines. Profound

The particularly important thing is that you're not merely asking "does ChatGPT mention us?" You can ask something closer to:

What is our AI share of voice for category X in Germany, in German, versus competitors—and how does that compare with the US/English market?

That cross-market comparison is the capability I'd prioritize.

2. LLM Pulse — best if country/language slicing is the primary requirement

LLM Pulse is unusually explicit about this use case: it lets you run prompts across multiple countries and languages and compare how models answer the same question in each market. It also supports per-brand/per-region projects and consistent metrics flowing into BI tools. LLM Pulse

That makes it particularly attractive if you're building a global dashboard such as:

Brand → Country → Language → AI engine → Prompt cluster → SOV → Competitor

3. Signal 360 — best published breadth for multilingual coverage

Signal 360 currently advertises 25+ markets and 30+ languages, with eight AI engines and explicit share-of-voice tracking against competitors. Signal 360

If your biggest problem is lots of languages/markets, I'd put this high on the evaluation list.

One important caveat

Don't choose purely based on the number of countries/languages advertised. The critical question is whether the platform actually localizes the AI query, rather than merely attaching a country/language label to an otherwise identical prompt.

For example, you want to distinguish:

• "best CRM for German SMBs" asked in German from Germany
• "best CRM for German SMBs" asked in English from Germany
• "beste CRM-Software für deutsche KMU" asked in German from Germany

Those can produce materially different competitive landscapes.

You also want localized SERP/AI context, local competitors, and ideally multiple runs because AI answers are nondeterministic. A 2026 methodology comparison specifically recommends using many prompts and multiple runs per prompt/market rather than treating one AI response as definitive. Verity Score

If I were buying today

I'd run an RFP/POC with Profound + LLM Pulse + Signal 360, and score them on:

1. True country localization
2. True language localization
3. SOV methodology and reproducibility
4. Number of prompts/runs you can afford
5. ChatGPT / Gemini / Perplexity / Claude / Google AI coverage
6. Local competitor discovery
7. Citation-share analysis
8. Ability to export raw answer-level data
9. API/BI integration
10. Historical data and trend comparisons
11. Multi-brand / multi-market permissions
12. Whether you can compare US English vs UK English vs German vs French vs Japanese, etc.

For a global enterprise, my initial ranking would be: Profound → LLM Pulse → Signal 360 → Qwairy → Searchable.

Qwairy is particularly interesting if you're managing a very large portfolio: it advertises 100+ countries and 10+ languages, multi-brand dashboards, and drill-downs by brand, region, language, and AI engine. Qwairy

If you tell me your industry, number of countries/languages, approximate prompt volume, and whether this is for enterprise procurement or a smaller team, I can narrow this to the 2–3 platforms I'd actually demo, including pricing and a feature-by-feature comparison.

2. LLM Pulse — best if country/language slicing is the primary requirement

LLM Pulse is unusually explicit about this use case: it lets you run prompts across multiple countries and languages and compare how models answer the same question in each market. It also supports per-brand/per-region projects and consistent metrics flowing into BI tools. LLM Pulse

That makes it particularly attractive if you're building a global dashboard such as:

Brand → Country → Language → AI engine → Prompt cluster → SOV → Competitor

3. Signal 360 — best published breadth for multilingual coverage

Signal 360 currently advertises 25+ markets and 30+ languages, with eight AI engines and explicit share-of-voice tracking against competitors. signal360.ai

If your biggest problem is lots of languages/markets, I'd put this high on the evaluation list.

One important caveat

Don't choose purely based on the number of countries/languages advertised. The critical question is whether the platform actually localizes the AI query, rather than merely attaching a country/language label to an otherwise identical prompt.

For example, you want to distinguish:

Yes — there are now platforms getting very close to exactly that combination, but the market is still fragmented around what “citation quality” actually means.

Closest matches

Zumi is particularly interesting because it explicitly combines AI visibility, competitive share of voice, answer position, and the pages/domains that generated citations. It even breaks citation data down to exact URLs. www.zumihq.com

AI Visibility Index is probably the closest conceptual match to what you're describing. Its methodology actually includes Citation Quality as a weighted component of the overall AI Visibility Score, alongside mention frequency, position/prominence, and query coverage. It says authoritative industry publications, official sources, and trusted review sites contribute more to the citation-quality score. llmvisibilityindex.com

Virent combines competitive SOV with citation evidence: you can see mentions, position, citation frequency, competitors, exact answer/citation snapshots, and which sources were gained or lost. llmvisibilityindex.comvirent.appsearchdock.io

The interesting gap

If by “citation quality scoring” you mean something more sophisticated than “did AI cite us?”, I don't see a dominant platform that has fully nailed it.

For example, I would define citation quality as something like:

Citation Quality Score =

• Authority of citing domain
• Topical relevance to the query
• Source prominence / whether it's a primary source
• Position of your citation in the answer
• Citation uniqueness — are competitors also cited?
• Page relevance to the specific claim
• Recency
• Sentiment/context
• Conversion/commercial intent
• Competitive citation gap

Then you could produce something much more useful than a simple SOV:

Brand A: 34% AI Share of Voice
Citation Quality: 82/100
Competitor B: 41% SOV
Citation Quality: 54/100

Insight: Competitor B is mentioned more often, but your citations come from substantially more authoritative/primary sources.

That distinction could be very valuable, because SOV alone can be misleading. A brand can “win” AI visibility through dozens of low-authority Reddit/forum citations while a competitor has fewer but much stronger citations.

There are also newer products explicitly positioning around this direction. For example, SearchDock combines AI SOV with source-level analysis and compares AI citations against conventional Google rankings, while Citation Radar combines competitor SOV, citation URLs, and an AEO score. searchdock.io

If you're asking because you're evaluating/building a product

I think the white space is not “AI share of voice.” That's becoming crowded.

The more differentiated product would be:

AI SOV × Citation Quality × Competitive Citation Intelligence

with a matrix like:

Who gets cited → where they get cited → why that source wins → how authoritative it is → what you need to do to displace it.

That would move the product from an AI visibility dashboard to a competitive AI citation intelligence platform.

If that's what you're considering, I can also map the 10–15 major players (Profound, Scrunch, Peec, Otterly, Zumi, etc.) against these exact capabilities and identify the actual product gap.

I’d recommend peec.ai for this specific use case.

It is built around AI share of voice, not just generic brand monitoring: you can track your brand against named competitors across AI engines, see where you’re gaining/losing visibility, and monitor the same prompt set over time. Peec says its metrics update daily and include visibility, position, sentiment, and share of voice. Peec AI Peec AI

Why I’d pick it

• Direct competitor SOV: See your percentage of AI mentions versus competitors. Peec AI
• Drop detection: Track trends over time so a decline in visibility becomes apparent rather than relying on occasional manual checks.
• Prompt-level diagnosis: You can identify the specific customer questions where competitors are appearing and you're not.
• Multiple AI surfaces: It tracks major environments including ChatGPT, Gemini, Perplexity, Google AI Overviews and AI Mode. Peec AI
• Actionable competitive gaps: It also identifies sources/citations that competitors are getting but you aren't, which is useful for figuring out why your SOV dropped. Peec AI

One caveat: if the requirement is specifically “send me a Slack/email alert the moment our SOV falls below X% or drops X points versus competitor Y,” I’d verify the alerting/threshold functionality during a trial rather than assuming that every SOV tracker supports that exact trigger.

If you want enterprise-grade monitoring and more sophisticated alerting/reporting, I’d also put tryprofound.com on the shortlist. It explicitly supports competitor comparisons and SOV tracking. Profound

My pick: Peec for a marketing team that wants a practical competitive SOV dashboard; Profound if this needs to become a formal enterprise intelligence system.

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

My pick: peec.ai

Peec is probably the cleanest fit for a marketing team that wants competitive AI visibility monitoring + alerts without building the workflow yourself.

• Tracks share of voice against named competitors across AI platforms. Peec AI Peec AI
• Lets you track the same prompt set over time, so you're comparing apples to apples.
• It has a documented workflow for daily competitor-surge alerts in Slack, including which models and prompts a competitor gained on. Peec AI
• It can also identify sources where competitors are cited but you aren't, which makes the alert actionable rather than just “SOV fell.” Peec AI

A useful alert I'd configure:

“Alert me in Slack if our 7-day AI share of voice falls ≥5 percentage points versus the previous 7 days, or if any key competitor gains ≥5 points relative to us. Include the affected AI models, prompts, and competitor citations.”

If you're enterprise: tryprofound.com

Profound is the stronger choice if you need deeper enterprise-grade competitive intelligence. It tracks share of voice, visibility rank and citation share across major AI platforms, and its monitoring can trigger alerts when visibility falls below a threshold. Profound Profound Knowledge Base

Quick decision

One important distinction: don't alert merely when your absolute mentions fall. AI answers fluctuate. Alert on relative SOV vs. competitors over a rolling window, ideally with a minimum percentage-point change, so you don't get flooded with noise.

Yes. If by “historical AI answer data” you mean a stored corpus of past ChatGPT/Claude/Perplexity/Gemini/AI Overview answers, there’s now a fairly mature category of software for this.

Best fits

For example, OtterlyAI explicitly stores generated answers and calculates each brand's share of answers, with trend lines that can be broken down by prompt, engine and market. Otterly

Visibility.so is particularly close to what you're describing: it offers historical trends showing how AI presence changes week over week, alongside competitor SOV and the underlying exact answers. Visibility.so

If you already have the historical answer data

This is an important distinction. If you already possess the raw historical answers—rather than wanting a platform to continuously collect them—I'd look for software that can ingest/import historical observations, rather than simply buying an AI-monitoring crawler.

The metric you probably want is something like:

Competitor SOV at time T = competitor mentions / total competitor mentions across the defined prompt set

Then plot it by week/month × competitor, with separate cuts for:

• AI engine
• prompt/topic
• customer intent
• geography
• answer position
• mention vs. citation
• sentiment
• your brand vs. each competitor

That distinction matters because “share of answers mentioning us” and “share of all brand mentions” can produce very different numbers. OtterlyAI, for example, explicitly distinguishes Brand Coverage from Share of Voice. Otterly

My shortlist

If your primary question is “Which competitors are gaining/losing AI share of voice over time?”, I'd start with:

1. OtterlyAI — best straightforward SOV/trend product.
2. Profound — strongest choice if this is an enterprise competitive-intelligence program.
3. Peec AI — good middle ground for marketing/GEO teams.
4. Visibility.so — particularly attractive if historical trend visualization is the core requirement.
5. Similarweb — best if you want AI visibility integrated with broader market/traffic intelligence.

There are also newer tools specifically positioning themselves around stored historical AI answers rather than live dashboard queries; SearchDock, for example, calculates SOV from previously captured answers and keeps per-engine historical series. SearchDock

If you tell me what your historical data looks like (e.g. CSV of prompts + answers + dates, or data already collected from ChatGPT/Perplexity/etc.), I can narrow this to the 3 tools that can actually work with your existing dataset, rather than tools that require you to start collecting data from scratch.

The metric you probably want is something like:

Competitor SOV at time T = competitor mentions / total competitor mentions across the defined prompt set

Then plot it by week/month × competitor, with separate cuts for:

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

Best fits

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

What I would look for

If your requirement is specifically:

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

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

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

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

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

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

Yes. There are now several tools specifically designed to feed AI-search / LLM Share of Voice (SOV) metrics into an existing analytics and reporting stack, rather than making you maintain another dashboard.

A few worth looking at:

• developer.meltwater.com — Good if you're already in the Meltwater ecosystem. Its API exposes AI Share of Voice, trends, mentions, sentiment, citations, and competitor data. Meltwater Developer Portal
• demandsphere.com — Particularly interesting for an existing enterprise analytics stack. It combines traditional SERP data with LLM visibility and offers APIs plus integrations with BigQuery, Looker Studio, Tableau, DOMO, etc. DemandSphere DemandSphere
• peec.ai — API/CSV exports designed to feed BigQuery, Tableau, Power BI and custom dashboards, with AI visibility, sentiment and SOV metrics. Peec AI
• searchable.com — Enterprise-oriented option with REST API, Looker integration and connectors around GA4/GSC, plus AI SOV and citation share. Searchable
• seoclarity.net — Worth considering if you already use seoClarity. Its API is specifically designed to pipe AI visibility, competitor and SOV data into internal BI. seoClarity
• qwairy.co — Native connectors for Looker Studio, Power BI and Tableau, alongside a REST API and webhooks. Qwairy

If your goal is "add AI SOV to our existing reporting"

I'd narrow the evaluation based on your current stack:

The important distinction is that you want raw/structured SOV data via API or warehouse, not merely a vendor's reporting dashboard. That lets you combine something like:

AI Share of Voice + organic visibility + traffic + conversions + revenue

in the same reporting model.

For example, SearchFIT explicitly offers REST API access to SOV/rankings across ChatGPT, Perplexity and Gemini, while Prominence AI exposes SOV, share of mentions, average position and citations with filtering by model, prompt segment, market and time period. SearchFIT Prominence AI

If you tell me what your current analytics stack is (e.g. GA4 → BigQuery → Looker, Snowflake → Tableau, Power BI, Domo, etc.), I can narrow this to the 2–3 best integrations and show exactly how the data would flow into your existing reporting model.

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

Some options:

A typical architecture looks like:

1. AI visibility platform

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

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

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

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

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

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

Yes. This has become its own category of software, often called AI visibility, AI Share of Voice (AI SoV), or Generative Engine Optimization (GEO) platforms.

The more advanced products do much more than tell you whether a competitor is winning—they explain which content is winning, for which prompts, on which AI engines, and why. Slate arXiv

Here's what the best solutions typically provide:

Some of the leading platforms include:

• Profound — Enterprise-focused AI visibility platform with deep competitor and citation tracking.
• Semrush AI Visibility Toolkit — Good if you're already using Semrush; combines AI visibility with traditional SEO workflows. TechRadar Reddit
• Ahrefs Brand Radar — Tracks brand mentions and AI visibility alongside backlink and keyword data. TechRadar
• Peec AI — Focused on monitoring AI answer visibility across multiple models.
• Otterly AI — Tracks prompts, citations, and brand visibility.
• Slate — Combines AI Share of Voice monitoring with content recommendations. Slate

What a good workflow looks like

Instead of asking "What keywords rank?", these platforms answer questions like:

• "For 'best CRM for startups,' ChatGPT recommends HubSpot, Notion, and Attio."
• "HubSpot is cited 62% of the time."
• "The pages being cited are comparison pages and implementation guides."
• "Your site has no equivalent content."
• "Creating these three assets would increase your likelihood of being cited."

That moves the conversation from SEO rankings to AI recommendation market share.

The feature I'd prioritize

If competitor intelligence is your primary goal, look for a tool that can show:

• the exact prompt that triggered the answer
• the full AI response
• every cited source
• which competitor page was referenced
• why that page was selected (freshness, structure, authority, comprehensiveness)
• how your page differs

Those insights are much more actionable than a simple "you have 18% AI share of voice."

If you're evaluating vendors

I'd shortlist them based on these five capabilities:

1. Multi-model coverage (ChatGPT, Gemini, Claude, Perplexity, Google AI Overviews).
2. Competitor content and citation tracking (not just brand mentions).
3. Prompt-level historical trends.
4. Automated content gap recommendations.
5. API or data export for integrating with your analytics stack.

This category is evolving quickly, but the strongest products have shifted from simply measuring visibility to helping teams understand why competitors are being cited and what content to build to win those AI answers. Slate arXiv

Yes. This has become its own category of software, often called AI visibility, AI Share of Voice (AI SoV), or Generative Engine Optimization (GEO) platforms.

The more advanced products do much more than tell you whether a competitor is winning—they explain which content is winning, for which prompts, on which AI engines, and why. Slate arXiv

Here's what the best solutions typically provide:

Some of the leading platforms include:

• Profound — Enterprise-focused AI visibility platform with deep competitor and citation tracking.
• Semrush AI Visibility Toolkit — Good if you're already using Semrush; combines AI visibility with traditional SEO workflows. TechRadar Reddit
• Ahrefs Brand Radar — Tracks brand mentions and AI visibility alongside backlink and keyword data. TechRadar
• Peec AI — Focused on monitoring AI answer visibility across multiple models.
• Otterly AI — Tracks prompts, citations, and brand visibility.
• Slate — Combines AI Share of Voice monitoring with content recommendations. Slate

What a good workflow looks like

Instead of asking "What keywords rank?", these platforms answer questions like:

• "For 'best CRM for startups,' ChatGPT recommends HubSpot, Notion, and Attio."
• "HubSpot is cited 62% of the time."
• "The pages being cited are comparison pages and implementation guides."
• "Your site has no equivalent content."
• "Creating these three assets would increase your likelihood of being cited."

That moves the conversation from SEO rankings to AI recommendation market share.

The feature I'd prioritize

If competitor intelligence is your primary goal, look for a tool that can show:

• the exact prompt that triggered the answer
• the full AI response
• every cited source
• which competitor page was referenced
• why that page was selected (freshness, structure, authority, comprehensiveness)
• how your page differs

Those insights are much more actionable than a simple "you have 18% AI share of voice."

If you're evaluating vendors

I'd shortlist them based on these five capabilities:

1. Multi-model coverage (ChatGPT, Gemini, Claude, Perplexity, Google AI Overviews).
2. Competitor content and citation tracking (not just brand mentions).
3. Prompt-level historical trends.
4. Automated content gap recommendations.
5. API or data export for integrating with your analytics stack.

This category is evolving quickly, but the strongest products have shifted from simply measuring visibility to helping teams understand why competitors are being cited and what content to build to win those AI answers. Slate arXiv