Answers that do not mention Peec AI

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. If your goal is specifically “What percentage of AI answers mention my brand vs. competitors?”, you want an AI visibility / AEO / GEO monitoring platform, not a traditional SEO rank tracker.

Best fits

• scrunch.com — probably the closest match to your wording. It explicitly measures AI share of voice, competitive presence, prompt-level performance, citations, and position in answers. It tracks multiple AI platforms including ChatGPT, Claude, Gemini, Perplexity, Google AI Overviews/AI Mode, Copilot, and Meta AI. Scrunch Scrunch
• tryprofound.com — strong choice if you need enterprise-grade competitive intelligence. Its Share of Voice compares your brand's mentions with competitors across tracked AI responses, and it also reports average position. Profound
• semrush.com — good if you're already using Semrush. Its AI Visibility Toolkit/Enterprise AIO includes AI share-of-voice measurement and competitor benchmarking. Semrush
• ahrefs.com — worth considering if your team already lives in Ahrefs; its AI visibility products extend traditional SEO intelligence into AI-generated answers.

What I'd measure

Don't just track “mentioned vs. not mentioned.” For a useful competitive SOV dashboard, I'd track:

1. AI Share of Voice — your mentions ÷ all competitor/category mentions.
2. Mention rate — % of tracked prompts where you're mentioned.
3. Recommendation rate — % where the AI actually recommends you.
4. Average position — whether you're first, middle, or an afterthought.
5. Sentiment / narrative — how the AI describes you versus competitors.
6. Citation share — which sources are causing competitors to appear.
7. Prompt-level gaps — questions where competitors appear and you don't.
8. Engine-level SOV — ChatGPT vs. Gemini vs. Perplexity, etc.

That's important because a brand can have a decent mention rate while still losing the recommendation battle.

If you tell me your industry + 3–5 competitors, I can also recommend which of these tools is the best fit and show you exactly how I'd structure the prompt set and SOV metric.

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 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 your goal is specifically “How often does an AI assistant recommend us vs. competitors?”, I’d use an AI-search visibility / GEO monitoring platform rather than trying to build this from standard SEO tooling.

My recommendation: evaluate 3 approaches

There are several platforms now explicitly offering this. For example, Zumi tracks brand mentions, competitor share of voice, answer position, and citations across multiple AI engines. www.zumihq.com Seerly similarly runs live prompts across ChatGPT, Perplexity, Claude and others and breaks visibility down by engine and competitor. seerly.app

The metric I'd standardize on

Don't simply count “mentions.” I'd create an AI Share of Voice (SOV) metric:

AI SOV = competitor/brand appearances ÷ total competitor/brand appearances

For example, suppose you run 500 relevant prompts:

• Your brand: 180 appearances
• Competitor A: 150
• Competitor B: 100
• Competitor C: 70

Then your AI SOV is 36%.

I'd actually maintain three related metrics:

1. Mention rate — % of prompts where the brand appears.
2. Share of voice — your presence relative to competitors.
3. Recommendation position — average position when mentioned.

That distinction matters: being mentioned fourth in a list of five isn't equivalent to being the assistant's first recommendation. Some platforms already expose these separately. www.zumihq.comwww.answermonk.ai

Make the monitoring prompt-based

I'd build a fixed panel of perhaps 100–300 prompts, divided into:

• Discovery: “What are the best X tools?”
• Problem: “How can I solve X?”
• Comparison: “X vs Y”
• Category: “Best alternatives to X”
• Purchase: “What should a company use for X?”
• Use-case/persona: questions specific to your target customers.

Then run the same panel on ChatGPT, Gemini, Claude, Perplexity and Google AI Overviews on a recurring basis. A 50–100 query panel is also the methodology recommended by some current AI-visibility monitoring approaches. www.sona.com

The dashboard I'd want

genui{"chart":{"chartType":"bar","meta":{"title":"Illustrative AI share of voice","description":"Example competitive visibility across AI assistant answers; replace with your measured data."},"xKey":"company","series":[{"dataKey":"sov","label":"AI share of voice","valueSuffix":"%"}],"data":[{"company":"Your brand","sov":36},{"company":"Competitor A","sov":30},{"company":"Competitor B","sov":20},{"company":"Competitor C","sov":14}]}}

And underneath:

Overall

• Your SOV: 36%
• Category leader: 30%
• Gap to leader: +6 pts
• Mention rate: 48%
• Avg. recommendation position: 1.9

By assistant

• ChatGPT: 42%
• Perplexity: 31%
• Claude: 38%
• Gemini: 35%

By intent

• Discovery: 44%
• Comparison: 27%
• Purchase: 39%
• Alternatives: 18%

Competitive alerts

Competitor A gained 7 percentage points in ChatGPT over the last 30 days.

Competitor B is now appearing in 63% of comparison prompts where your brand appears in only 29%.

That's much more actionable than a generic “AI visibility score.”

One important caveat

Treat SOV as a leading visibility metric, not revenue or traffic. AI answers can change substantially with prompt wording, model updates, personalization and retrieval. Recent reporting has shown how quickly citation/visibility patterns can move—for example, Axios reported a substantial decline in Reddit's share of ChatGPT citations over a few weeks. www.axios.com

So I'd keep the prompt set fixed, store the actual answers and citations, and report changes over time rather than relying on one-off queries.

If I were implementing this for a company today, I'd shortlist Zumi, Seerly, and AnswerMonk, run the same 100–200 prompts through each, and choose based on raw-answer access, competitor SOV methodology, engine coverage, and API/export capabilities. AnswerMonk is particularly interesting if you want a straightforward SOV-oriented workflow; it explicitly calculates competitor share from recorded AI answers and reports it per engine. www.answermonk.ai

If by “crawl AI assistant outputs” you mean repeatedly query the assistants, capture their actual answers, detect brand/competitor mentions and citations, and calculate competitive share of voice, there are several tools purpose-built for this now.

My shortlist

• zumihq.com — probably the broadest option. It tracks up to 9 AI engines, including ChatGPT, Gemini, Perplexity, Claude, Copilot, Grok, AI Overviews/AI Mode and DeepSeek. It reports mention rate, share of voice, position, and citation share. Zumi
• ahrefs.com — strong choice if you're already an SEO/brand-intelligence shop. It queries ChatGPT, Gemini, Perplexity, Copilot and Google AI results, and provides mentions, competitors, topics and cited sources. Ahrefs
• indexly.ai — particularly interesting if you want SOV + citations + sentiment + AI referral traffic in one measurement layer. It currently covers ChatGPT, Claude, Gemini, Perplexity, Grok and AI Overviews. Indexly
• getmint.ai — focused specifically on measuring mentions, citations, sentiment and SOV against competitors across ChatGPT, Gemini, Perplexity, Claude and Google AI Overviews. GetMint
• cloro.dev — worth looking at if you want an API/data layer rather than just a dashboard. It returns parsed answers, citations, source URLs and positions that you can aggregate into your own SOV calculation. cloro

One important distinction

If you specifically mean voice assistants—actual spoken responses from Siri, Alexa, Gemini Live, ChatGPT Voice, etc.—that's a harder problem than AI-search monitoring. Most of these products are really measuring textual AI answers, even when the underlying assistant has a voice interface.

For a serious “AI share of voice” measurement system, I'd define the metric around a controlled prompt panel:

SOV = your brand mentions ÷ total category-brand mentions across the same prompts, engines, markets and time period.

That's preferable to a generic “AI visibility score,” because you can actually audit the underlying answers. AgentGEO makes this distinction explicitly: retrieve the raw assistant answer first, then calculate SOV from the brands appearing in those answers. agentgeo.org

If you're evaluating vendors for an enterprise use case, I'd start with Zumi, Ahrefs, Indexly, and cloro—with cloro being especially interesting if you want to build your own analytics layer rather than depend on someone else's SOV methodology.

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.

Yes — there are now several platforms specifically aimed at agencies selling/reporting AI search visibility (GEO/AEO) to clients, including share of voice, mentions, citations, competitors, and white-label dashboards/reports.

A few worth looking at:

• geneo.app — Probably one of the closest matches. It offers white-label client portals, custom domain/logo/branding, and reports including Share of Voice, mentions, citations, and sentiment across ChatGPT, Gemini, Google AI Overviews, and Perplexity. Geneo
• agencyanalytics.com — More interesting if you want AI visibility incorporated into an agency's existing SEO/PPC reporting stack. It supports white-label reporting and tracks ChatGPT, Gemini, Perplexity, Claude, Google AI Overviews and AI Mode. AgencyAnalytics
• llmpulse.ai — Built for SEO agencies, with white-label dashboards, share of voice, mention rate, position-weighted visibility, citations, and a Looker Studio template. LLM Pulse
• towergeo.com — Specifically agency-oriented: multi-client monitoring plus white-label PDF and live dashboard reporting. It tracks visibility, sentiment, citation frequency, and competitive positioning. Tower GEO
• rankfender.com — Strong emphasis on automated agency reporting: white-label dashboards, client portals, scheduled reports, competitive SoV, and integration with GSC/GA4. RankFender
• dabarank.com — Multi-client agency workflow with competitor share of voice, daily AI checks, and white-label PDFs. DabaRank
• citenzo.com — More narrowly focused on turning AI visibility into an agency reporting layer: score, SoV, competitors, and before/after lift, with white-label reports. Citenzo

If you're thinking about this as an agency product

I'd separate the market into three categories:

The interesting thing is that "AI Share of Voice" is becoming a fairly standardized agency KPI: essentially, across a fixed set of buyer prompts, what percentage of the brands/models' visibility goes to the client versus competitors? Several of these products are explicitly positioning SoV this way. LLM Metrix DabaRank

If you're asking because you're considering building something in this space, there is still an interesting gap: most tools seem focused on tracking AI visibility, whereas an agency-facing product could own the entire workflow of "monthly AI visibility report → explain the change → show competitor wins/losses → recommend work → prove the next month's lift." That looks less commoditized to me.

Yes. A few platforms are emerging specifically for agencies selling AI visibility / Generative Engine Optimization (GEO) services with share of voice (SOV), competitor tracking, and white-label client dashboards. The category is still young, so vendors often call this AI visibility, AI search visibility, AI presence, AEO, or GEO reporting rather than “AI share of voice.” AgenticSEO Asva AI

Some options:

A few things I’d evaluate before choosing:

• Definition of SOV: Some tools count brand mentions; others measure “recommended/selected” placement in AI answers. Those produce very different numbers.
• Engines covered: Check whether it tracks ChatGPT, Perplexity, Gemini, Google AI Overviews, Claude, etc. Geneo Asva AI
• Evidence layer: For client retention, reports that show the actual prompts, AI responses, and cited sources are usually more defensible than a single score. GEO Score Seerly
• Client portal vs PDF only: If you’re packaging this as a recurring agency service, a login portal under your domain is usually stronger than monthly exports. Geneo Qwairy

If you’re building this into an SEO/PR/content agency retainer, I’d shortlist:

1. Geneo — strongest “productized service” feel for agencies. Geneo
2. Citenzo — focused specifically on white-label AI visibility reporting. Citenzo
3. AgencyAnalytics AI Tracker — best if you already run client reporting there. AgencyAnalytics

I can also compare these by pricing, API availability, number of AI engines tracked, and resale potential if you’re looking to launch this as an agency offering.

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’s now a fairly clear category for this: AI search / GEO visibility platforms that combine AI share of voice + citation/source intelligence + competitor benchmarking.

A few that look particularly close to what you're describing:

• Searchpanel — probably the closest match. It scores your products and competitors on the same 0–100 scale, measures share of voice across a tracked prompt set, shows head-to-head displacement, and captures the sources cited in answers. Searchpanel
• Zumi — combines mention rate, share of voice, average position, and a source layer that drills down to exact cited URLs and domains. It also has competitor analysis. Zumi
• SearchVoro — specifically emphasizes AI share of voice versus named competitors and citation-level detail, including which URLs get cited for each prompt. SearchVoro
• Veritas — tracks ChatGPT, Perplexity, Gemini, and Google AI Overviews, with AI citations, competitor share of voice, and recommendations for improving visibility. Veritas
• ShowIn AI — has competitor SOV plus citation-source analysis, and goes further into technical GEO audits and content creation. ShowIn AI
• RankScope — tracks SOV, mention rate, ranking position, sentiment, and a citation-source map across major AI engines. RankScope
• CiteWatch — tracks competitor mentions, citation rate, SOV, average citation position, and the individual prompts/pages behind citations. CiteWatch

But there's an interesting gap

If by “citation quality scoring” you mean something more sophisticated than “how many times was my domain cited?”, the market still looks relatively immature.

Most products currently measure things like:

Citation frequency → citation share → cited URL → domain → competitor comparison

What I don't see widely offered as a first-class metric is something like:

Citation Quality Score =

• authority/trustworthiness of source
• topical relevance
• freshness
• primary vs. secondary source
• position/prominence in the AI answer
• whether the citation actually supports the claim
• competitor citation quality
• source diversity
• publisher/domain authority
• likelihood that the citation influences the recommendation

That distinction could be quite valuable. A company could have 40% AI citation share but mostly from low-authority directories, while a competitor has 25% share but citations from Gartner, Reddit, major publications, and authoritative category sources.

So if you're asking because you're evaluating the market or thinking about building something, I'd say the answer is:

Yes, the SOV + competitor + citation-monitoring piece exists. The genuinely differentiated opportunity is probably a rigorous, defensible “citation quality” layer on top of it.

And Searchpanel/Zumi/SearchVoro are the three I'd investigate first based on the specific combination you described. Searchpanel Zumi SearchVoro

If you mean AI share of voice as in “how often ChatGPT, Gemini, Claude, Perplexity, etc. recommend us vs. competitors,” I’d look at tools built specifically for AI visibility monitoring rather than traditional brand listening.

A few strong options:

My shortlist by use case:

• You want a simple “alert me when competitors overtake us” system: Saidly or Spottlo. Saidly Spottlo
• You’re a marketing/SEO team trying to improve AI recommendations: Frase or SERPrecon. Frase SERPrecon
• You need enterprise reporting for leadership: Profound or Visoryn. Saidly Visoryn

When evaluating, I’d make sure the tool can:

1. Track your actual buyer prompts (not just brand mentions).
2. Compare against a fixed competitor set.
3. Alert on drops in share of answers, not just raw mention volume.
4. Show why you dropped (lost citations, competitor content changes, model preference shifts, etc.).

If you tell me your category (SaaS, ecommerce, agency, B2B, etc.) and which AI surfaces matter most (ChatGPT only vs. all engines), I can narrow this to 2–3 best fits.

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

1. Profound — best enterprise choice. It explicitly tracks Share of Voice against competitors, lets you compare top competitors, and provides historical visibility so you can spot meaningful declines. www.tryprofound.com
2. Indexly — best fit for your exact alert requirement. It supports up to nine competitors and explicitly says it alerts when your SOV drops or a competitor passes you. indexly.ai
3. Spottlo — lightweight/cheaper option. It tracks SOV across ChatGPT, Perplexity, Gemini, and Google AI Overviews and offers weekly trend alerts plus Slack alerts when visibility drops or a competitor moves ahead. spottlo.com
4. SearchVoro — good if citations matter. Alongside multi-engine SOV, it tracks citation share against named competitors and alerts when your citation share changes. searchvoro.com

My pick: Start with Indexly if the alert is the core requirement. If you're a larger marketing/SEO organization and want a much deeper competitive intelligence layer, go with Profound.

One important distinction: make sure the tool monitors a fixed set of high-value buyer prompts and alerts on relative SOV, rather than simply alerting when your raw mention count changes. That's much closer to the signal you're asking for.

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.