Answers that mention Peec AI

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

A few worth looking at:

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

What I'd measure

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

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

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

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

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

If you mean “what % of AI-generated answers mention/recommend my brand vs. competitors?”, you want an AI visibility / AI search analytics platform, not traditional SEO rank tracking.

My shortlist

• otterly.ai — probably the best straightforward fit. It tracks share of voice, brand mentions, sentiment, citations, and competitors across ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini, Copilot, and Claude. Otterly Otterly
• semrush.com — good if you're already using Semrush. Its Brand Performance reporting calculates AI SoV using both mention frequency and position, and benchmarks you against competitors. Semrush
• Profound — worth considering for enterprise-grade monitoring, especially if you need granular prompt, source, citation, and competitive analysis. Independent 2026 benchmarking also places it among the leading platforms. Benchline Reports Tomek Sikora
• Peec AI — particularly interesting if competitive share-of-voice analysis is the main objective; a 2026 benchmark specifically identified it as strong on competitive SoV. Benchline Reports

What I'd measure

Don't use just “mentions.” I'd build an AI SoV dashboard around:

AI Share of Voice → Brand Coverage → Recommendation/Position → Sentiment → Citations → Competitor Gap

For example:

Across 1,000 category prompts, Brand A appears in 31% of answers, Competitor B in 24%, Competitor C in 18%, etc.

Then break that down by AI engine, prompt/topic, country, competitor, and time period. This is important because AI answers are dynamic, so a one-off manual test isn't a reliable measurement. Semrush Cognizo

If I had to pick one to test first: OtterlyAI. It is unusually explicit about the exact metric you're asking for—share of AI answers naming your brand versus competitors—and provides the underlying mention/citation data rather than just a generic “AI visibility” score. Otterly

If you tell me your industry + 3–5 competitors + whether you're SMB/mid-market/enterprise, I can also give you a side-by-side comparison of the best 3 tools for your specific use case.

If your core question is “How often does our product get mentioned or recommended in AI answers versus competitors?”, you’re looking for an AI visibility / GEO (generative engine optimization) tracking platform.

My shortlist for 2026:

Independent 2026 comparisons consistently identify Profound, Peec, and Otterly as leading dedicated options, while Ahrefs and Semrush make more sense if you already have those SEO ecosystems. Baarely TechnologyAdvice Marqeable

What I'd measure

Don't just track raw mentions. Set up a fixed set of real buyer prompts such as:

• “Best [product category] for a 50-person company”
• “[Product category] alternatives”
• “[Competitor] vs [your product]”
• “What software should I use for [job your product solves]?”
• “Best [category] for [specific use case]”

Then track, over time:

• Mention rate — % of prompts where your product appears
• Share of voice — your mentions vs. competitors
• Recommendation rate — % where the AI actually recommends you
• Position/rank in the answer
• Competitors appearing instead
• Citation rate — how often your site/content is cited
• Sentiment/description accuracy
• Which sources influence the answer

These are essentially the metrics the current AI-visibility platforms are designed around. Sapian Metrics NeuralGen

If I were evaluating vendors for your specific use case, I'd start with Profound, Peec, and Otterly, then compare their actual output on your 50–100 buyer prompts rather than relying on vendor-reported scores. AI responses are nondeterministic, and different platforms use different prompt samples, so their absolute “visibility” numbers aren't necessarily directly comparable. Marqeable

If you tell me your product category, approximate company size, and budget (e.g. <$100/mo, $100–500/mo, or enterprise), I can narrow this to 2–3 tools and compare current pricing/features.

If your main KPI is “How often does our product get mentioned/recommended in AI answers versus competitors?”, you’re looking for an AI visibility / GEO tracking platform rather than a traditional SEO rank tracker.

My shortlist

• peec.ai — strongest fit for your exact use case. It tracks visibility, mention frequency, position, sentiment, and share of voice, with competitor comparisons across AI engines. It also lets you build a prompt set around the queries that matter to your product. Peec AI Peec AI
• otterly.ai — strong dedicated monitoring option. It tracks your brand and named competitors across ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini, Copilot, and Claude, including mentions, share of voice, sentiment, and citations. Otterly
• semrush.com — best if you already use an SEO/content stack. Its AI visibility capabilities are integrated with broader SEO and content workflows, so you can connect AI visibility with conventional search data. Semrush
• Profound — worth considering for a larger enterprise marketing/SEO operation. It is consistently included among the major enterprise-oriented AI-search monitoring platforms in current 2026 comparisons. TechnologyAdvice Refine AI

What I would measure

For your use case, I'd set up a dashboard around:

The important distinction is mention frequency vs. share of voice. For example, if your product appears in 40% of answers and a competitor appears in 50%, that's more informative than simply tracking whether you appeared at all. Peec and Otterly both explicitly support this kind of competitive measurement. Otterly Peec AI

If I were evaluating only these tools for your stated requirement, I'd start demos with Peec AI and OtterlyAI, then compare them against Semrush if you already have a Semrush subscription.

If you tell me B2B/B2C, approximate number of products, and whether you care mostly about ChatGPT or all AI search engines, I can narrow this to the most appropriate 2–3 and compare pricing/features.

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

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

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

Yes. I’d recommend treating this as an AI-answer competitive intelligence / GEO monitoring problem rather than traditional SEO rank tracking.

Recommended solution

For a serious marketing team, I’d start with tryprofound.com or peec.ai and build your reporting around a fixed prompt universe.

Current AI-visibility platforms can monitor brand mentions, competitors, citations, rankings, and share of voice across ChatGPT and other answer engines. TechnologyAdvice The Rank Masters

The metric I would use

Define AI Share of Voice (SOV) as:

Your brand mentions ÷ total tracked competitor mentions × 100

For example, if across 1,000 relevant AI answers you get 180 mentions, while competitors get 420 + 250 + 150:

• Your brand: 18% SOV
• Competitor A: 42%
• Competitor B: 25%
• Competitor C: 15%

This is preferable to simply counting whether you're mentioned, because it tells you who is winning the recommendation space. Several current platforms use essentially this approach. AnswerMonk Siftly

What I'd monitor

Don't just run generic prompts like "best X software." Build a representative prompt panel:

• Category discovery: "What are the best X tools?"
• Comparison: "X vs Y"
• Problem/solution: "How do I solve [customer problem]?"
• Use case: "Best X for [industry/use case]"
• Buying intent: "What should I consider when buying X?"
• Alternative intent: "Alternatives to [competitor]"
• Brand-specific: "[Your brand] reviews / pricing / alternatives"
• Negative/problem prompts: "What are the drawbacks of X?"

Then run the same prompts repeatedly across ChatGPT, Perplexity, Gemini, Claude and Google AI Overviews/AI Mode, rather than treating one ChatGPT response as representative. AI answers are nondeterministic, so repeated sampling and fixed prompt sets are important. GeoEcho SourceWatch

Dashboard I'd build

Your executive dashboard should have:

The citation gap is particularly actionable: if ChatGPT repeatedly recommends Competitor A because it cites three review sites and comparison articles that never mention you, you have a concrete content/PR target. Current platforms increasingly expose this underlying citation data rather than just the headline score. RankZero Foglift

My preferred architecture

If you want something more sophisticated than buying a dashboard:

Prompt database → AI engine runner → response archive → entity/brand extraction → ranking + sentiment → citation extraction → SOV calculation → dashboard/alerts

I'd store every observation as:

date | engine | prompt | response | brand | mentioned | position | sentiment | cited_source | competitor_set

That gives you an auditable dataset rather than a mysterious "AI visibility score."

One important measurement rule

Keep the denominator fixed.

If you change your prompt set, competitors, engines, or measurement period, your SOV can move even though your actual visibility hasn't. This is a major issue with AI-answer measurement, and current monitoring vendors explicitly emphasize fixed prompt sets and comparable measurement windows. Foglift

So I'd report:

AI SOV = 27.4% (+4.2pp MoM)

rather than just:

AI visibility = 74

That makes the metric much easier to benchmark against competitors and manage like a conventional market-share KPI.

Which solution I'd choose

• Enterprise / serious GEO program: Profound
• Focused AI-search monitoring: Peec AI
• Need a simpler SOV-oriented product: Siftly, AnswerMonk, or SourceWatch
• Want to build proprietary competitive intelligence: API-driven prompt collection + your own warehouse/dashboard

There are now enough purpose-built products that I wouldn't build the entire collection layer from scratch unless your competitive intelligence requirements are unusual. Current market comparisons include Profound, Peec, OtterlyAI, Scrunch, Ahrefs Brand Radar, Semrush and others. TechnologyAdvice

If you tell me your industry + 3–10 competitors + which AI assistants matter, I can design the exact prompt taxonomy, SOV formula, dashboard KPIs, and weekly reporting framework I'd use.

Yes. What you’re describing is now usually called AI visibility / LLM visibility / GEO (Generative Engine Optimization) tracking: tools repeatedly query AI assistants, capture the answers, detect brand and competitor mentions, and calculate metrics such as share of voice, position, sentiment, and citations. AI Visibility Software ModelMention

A few tools worth looking at:

• tryprofound.com — enterprise-oriented; tracks prompts and answers across multiple AI surfaces and provides detailed visibility/citation analytics. Tomek Sikora DataDab
• peec.ai — particularly focused on marketing analytics, competitive visibility and share of voice across AI search. Arbling
• otterly.ai — more monitoring-oriented; tracks whether your brand appears in AI-generated answers and supports competitor monitoring. Toolsolved DataDab
• ahrefs.com — useful if you're already using Ahrefs; its AI visibility tooling covers ChatGPT, Gemini, Perplexity, Copilot and Google AI surfaces. Ahrefs
• searchprism.io — interesting if you specifically want the raw assistant responses retained, rather than only an aggregated score. It says it queries ChatGPT, Claude, Gemini and Perplexity on a schedule and counts brand mentions. Prism

One important distinction

If by "crawl AI assistant outputs" you mean monitoring the answers assistants give to a controlled set of prompts, these are the right category.

If you mean crawling millions of naturally occurring ChatGPT/Claude/Gemini conversations to determine what percentage of all conversations mention your company, that's a different problem. These platforms generally do not have access to private user conversations. Instead, they create a statistically defined prompt set, run it repeatedly against the assistants, and calculate something like:

Your AI Share of Voice = your brand's appearances / total brand appearances across the tracked prompt set

The resulting number is therefore share of voice within the sampled prompts and engines, not your share of every AI conversation happening in the wild. This distinction is important when presenting the metric internally. RocketBlue

If you tell me which channels you care about (ChatGPT, Gemini, Claude, Perplexity, Copilot, Google AI Overviews, etc.) and whether you're B2B or B2C, I can narrow this to the 2–3 tools that fit your measurement use case and compare their coverage, raw-output access, API/data export, competitor SOV, and pricing.

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

A few current options:

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

One important distinction

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

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

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

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

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

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

As of September 2026, its entry plan is listed at $29/month for 15 tracked prompts, with tracking across ChatGPT, Google AI Overviews, Perplexity, and Microsoft Copilot. That makes it a relatively low-cost way to establish a baseline for how often your products/brand are mentioned and cited in AI answers. AEO Labs CiteGraph

Quick comparison

Semrush's current AI Visibility base plan is $99/month per domain and includes 25 custom prompts plus visibility data from ChatGPT, Google AI, Gemini, and Perplexity. Semrush

What I'd actually track

Don't waste your 15 prompts on generic queries like "best ecommerce stores." For an ecommerce business, make them commercial and product-specific, for example:

• “Best [product category] for [use case]”
• “What are the best [product type] for [audience]?”
• “[Product category] brands recommended by experts”
• “Where should I buy [product]?”
• “[Competitor] alternatives”
• “Best [product] under $100”
• “Is [your brand] a good option for [use case]?”

Then watch three numbers:

AI citation share → how often your site gets cited
Mention/share of voice → how often your brand appears
Which pages get cited → what content/products AI actually trusts

For a small store, the third metric can be especially actionable: if AI repeatedly cites competitors' buying guides but never your product/category pages, you've got a concrete content/SEO target.

One caveat: 15 prompts is pretty small, so I'd treat the $29 plan as a measurement experiment, not a comprehensive representation of your entire AI visibility. If you eventually need dozens/hundreds of commercial queries, costs rise quickly. CiteDaily

My budget-conscious setup: Otterly at $29/month + Google Search Console + your analytics. I'd only move to Peec or Semrush once you can identify a specific reporting/competitive-analysis capability you're missing.

If you tell me your product category and monthly marketing budget (e.g. <$50, $100, $250), I can suggest the exact 15 prompts I'd use to measure your AI citation share.

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

As of September 2026, its entry plan is around $29/month for 15 tracked prompts across ChatGPT, Google AI Overviews, Perplexity, and Microsoft Copilot. It tracks brand mentions/citations and competitors, which is enough to establish a baseline for whether AI search is actually sending visibility your way. AEO Labs CiteGraph

How I’d compare the main options

Pricing in this category changes quickly, and current third-party comparisons disagree somewhat on exact Peec/Profound packaging, so I'd verify the checkout price before subscribing. AEO Labs CiteTrack AI

For ecommerce specifically

Don't just track "Does ChatGPT mention my store?" I'd create 10–15 buyer-intent prompts such as:

• "Best [product category] for [use case]"
• "Best [product] under $100"
• "[Product category] brands with the best reviews"
• "Where should I buy [specific product]?"
• "[Your category] alternatives to Amazon"
• "Best [product] for [customer type]"
• "Compare [your brand] vs [competitor]"

Then track three things:

1. Mention share — how often your store/brand appears.
2. Citation share — how often AI actually links to your site.
3. Competitor presence — which competing stores/products appear instead.

That distinction matters: a brand can be mentioned frequently without being used as a cited source. AEO Labs Ayzeo

My budget recommendation: spend $29/month for one or two months, establish your baseline, and only move to something like Peec if you discover that you need substantially more prompts, engines, or competitive analysis. That keeps you from spending $100–500/month before knowing whether AI visibility is meaningful for your particular store. AEO Labs

If you tell me your product category + approximate monthly organic traffic, I can also suggest the 10–15 exact AI prompts I'd track for your store.

Yes. This is now a fairly established category, usually called AI visibility tracking, AEO (Answer Engine Optimization), or GEO (Generative Engine Optimization).

A few tools specifically offer the combination you're describing:

• tryprofound.com — particularly enterprise-oriented. Tracks brand visibility, citations, share of voice, visibility rank, and competitor performance across ChatGPT, Gemini, Perplexity, Google AI Overviews, Copilot, Grok, and others. It can identify competitors based on who actually gets cited for your tracked prompts. Profound
• ansvisor.com — explicitly focused on competitor benchmarking at the prompt, answer, citation, and platform levels. Ansvisor
• cognizo.ai — tracks mentions/citations and lets teams benchmark visibility and perception against category leaders and competitors. Cognizo
• citationly.ai — enterprise-focused tracking across ChatGPT, Gemini, Claude, Perplexity, Copilot and Grok, with competitor benchmarking and share-of-voice reporting. CITATIONLY
• hubspot.com — a more accessible option that compares your visibility against competitors across ChatGPT, Gemini and Perplexity. HubSpot

What these platforms actually measure

The useful ones go beyond "does ChatGPT mention my company?" They can measure things like:

• Visibility rate: percentage of tracked AI answers mentioning you
• Share of voice: your mentions versus competitors
• Position/rank: where you're appearing in an AI recommendation
• Citation share: how frequently AI cites your domain versus competitors
• Prompt-level gaps: questions where competitors appear but you don't
• Competitor discovery: brands you weren't necessarily considering as competitors but that AI recommends
• Source analysis: which publications/sites AI relies on when recommending competitors
• Sentiment/narrative: how AI describes your brand
• Engine-by-engine performance: ChatGPT vs. Gemini vs. Perplexity, etc.

Profound, for example, explicitly supports competitor benchmarking by topic, prompt, and AI platform, while Ansvisor lets you inspect competitor performance down to individual AI answers. Profound Ansvisor

If you're evaluating this as an enterprise buyer

I'd separate the market into three tiers:

The important distinction is measurement methodology. AI answers aren't deterministic like traditional Google rankings, so an enterprise platform needs a well-designed, persistent prompt set, consistent model/version tracking, geographic controls, historical data, and transparent definitions of "visibility." Some newer platforms explicitly emphasize this issue; for example, Searchestra says it uses versioned buyer questions and reports missing data rather than inventing it. Searchestra

If you're asking because you're thinking about building/buying a product in this category, I can also map out the current competitive landscape—Profound vs. Peec vs. Scrunch vs. Otterly vs. Athena vs. Semrush/Ahrefs, including what each measures, enterprise features, pricing, and where the gaps in the market appear to be.

Yes. A new category of tools has emerged around AI visibility / Answer Engine Optimization (AEO) / Generative Engine Optimization (GEO). These platforms track whether your brand appears in AI-generated answers, how often competitors appear, what sources are cited, and how visibility changes over time. Ansvisor

Some tools enterprise teams are evaluating:

Typical enterprise use cases:

• Share of AI voice: “When buyers ask AI about our category, how often do we appear versus competitors?”
• Prompt-level benchmarking: Compare the same buyer questions across ChatGPT, Gemini, Perplexity, Claude, Copilot, etc.
• Citation analysis: Identify which publications, pages, communities, and domains are causing competitors to appear.
• Answer monitoring: Capture how AI describes your products, positioning, strengths, and weaknesses.
• Executive reporting: Provide dashboards for marketing, SEO, product, and brand teams. Ansvisor TechnologyAdvice

When evaluating one for an enterprise team, I’d look for:

1. Repeatable prompt sets (not just one-off audits)
2. Historical trend data
3. Competitor tracking
4. Citation/source visibility
5. Export/API access
6. Multi-brand, multi-region, and permissions support

If you mean “visibility inside ChatGPT specifically” (rather than AI search broadly), the shortlist changes somewhat because you’ll want tools that capture actual ChatGPT answer behavior and citations rather than only AI-overview tracking. geolyze.org

If your specific goal is “I updated content, then I want to know whether my AI-search share of voice changed”, I’d focus on tools that let you maintain a fixed prompt set and compare SoV/citations over time.

Tools worth considering

• otterly.ai — probably the most straightforward fit. It tracks mentions, citations, competitor share of voice, and the URLs being cited across multiple AI engines. Its own published experiment demonstrates a before/after SoV measurement against a fixed set of 25 prompts. Otterly Otterly
• peec.ai — useful if you want more competitive/citation analysis around your tracked prompts. It is positioned as a mid-market AI-search analytics platform with prompt research and competitor monitoring. DataDab
• tryprofound.com — better suited to larger organizations wanting granular prompt, citation, source, and competitive analysis. It also has content/authoring workflows, so it can fit a more sophisticated measurement → optimization loop. DataDab
• athenahq.ai — worth investigating if you want SoV monitoring combined with content-oriented workflows and prompt prioritization. DataDab
• higoodie.com — interesting if you want measurement closer to the content-production workflow; it combines AI-search monitoring with content creation capabilities. Refine AI

The important part: measure the change, not just the score

I'd set it up as an experiment:

1. Define a fixed set of, say, 50–200 buyer prompts.
2. Record baseline SoV and citations for 2–4 weeks.
3. Update the target pages.
4. Keep the prompt set unchanged.
5. Continue tracking for 4–8 weeks.
6. Compare:

• AI share of voice
• citation rate
• average position/order in answers
• which URLs are cited
• competitor mentions
• SoV by individual prompt, not merely the aggregate.

This distinction matters because AI visibility can vary substantially by engine and prompt. For example, Otterly's documented experiment showed materially different changes across ChatGPT, Gemini, Perplexity, Google AI Mode, and AI Overviews from the same underlying content experiment. Otterly

For your use case, I'd shortlist Otterly, Peec, and Profound first. The deciding question is less “which has an SoV metric?”—several do—and more whether you can preserve a controlled prompt set and connect a specific content update to a subsequent change in citations/SoV.

If you tell me roughly your site size, monthly budget, and whether you care about ChatGPT/Perplexity/Google AI specifically, I can narrow this to 2–3 options and compare their actual measurement workflows and pricing.

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

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

What I'd look for

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

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

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

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

Yes. The category you’re looking for is usually called AI visibility / GEO (Generative Engine Optimization) tracking.

Two tools I’d look at first:

• peec.ai — tracks brand mentions, visibility %, position, sentiment, share of voice, and citations across multiple AI engines. Its current coverage includes ChatGPT, Gemini, Perplexity, Google AI Overviews/AI Mode, Copilot, and additional models such as Claude, Grok, Mistral, DeepSeek, and Qwen on applicable plans. Peec AI Peec AI
• tryprofound.com — particularly relevant if you want an enterprise-oriented analytics layer. Its Answer Engine Insights measures visibility, sentiment, citations, and share of voice across major answer engines, including ChatGPT, Perplexity, Claude, Gemini, Google AI Overviews, Copilot, and others. Profound Help Center Profound

What I'd measure

For a brand, I wouldn't reduce this to a single "AI visibility score." Track at least:

• Mention rate: % of tracked answers that mention your brand
• Share of voice: your mentions relative to competitors
• Position/prominence: where you appear when multiple brands are recommended
• Citation share: how often your site/content is actually cited
• Sentiment/perception: how the AI describes you
• Engine-by-engine performance: ChatGPT vs. Gemini vs. Claude vs. Perplexity, etc.
• Prompt-level performance: which customer questions cause you to appear—or disappear
• Trend over time: whether visibility is increasing or declining

Peec, for example, explicitly reports visibility as the percentage of AI responses mentioning the brand and breaks the data down by engine. Peec AI Profound similarly calculates visibility from the responses generated for a controlled set of prompts. Profound Help Center

One important caveat: these tools generally measure sampled/controlled prompt responses, not the literal percentage of all real-world AI conversations in existence. So I'd treat the number as a consistent visibility index, not as "X% of people using ChatGPT saw our brand." Aiso

If you tell me your industry, approximate company size, and which assistants matter most (e.g. ChatGPT + Gemini + Claude + Perplexity), I can narrow this down to the 2–3 tools that fit and compare their coverage, methodology, integrations, and pricing.

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

Some tools to evaluate:

When comparing vendors, look for these metrics:

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

For a brand team, a practical setup is often:

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

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

For long-tail SaaS keywords, I’d look at AI-visibility/GEO tools that let you define your own prompts rather than relying only on a prebuilt keyword universe.

• Profound — strongest fit if you want granular prompt-level tracking, citations, competitors, and visibility across multiple AI engines. It is positioned toward deeper/enterprise AI-search analytics. Benchline Reports DataDab
• Peec AI — good fit for a SaaS team that wants competitive share-of-voice + prompt research without going as enterprise-heavy. Benchline Reports DataDab
• Otterly.AI — more lightweight/self-serve and explicitly geared toward tracking individual prompts and AI-search visibility; useful if you have hundreds of niche queries you want monitored. DataDab Marqeable
• Semrush AI Toolkit / Ahrefs Brand Radar — worth considering if you're already using those ecosystems. They add AI visibility to conventional SEO research, although their measurement model is somewhat different from dedicated GEO platforms. DataDab

For your specific use case

If by “long-tail query share of voice” you mean something like:

“best SOC 2 compliance software for a 20-person startup”
“alternatives to [competitor] for healthcare SaaS”
“lightweight CRM for a 5-person consulting firm”

…and you want:

1. Query → 2. AI answer → 3. brands mentioned → 4. citations → 5. your % of answers → 6. change over time

then I'd shortlist Profound, Peec, and Otterly.

The key question when evaluating them is whether they let you upload/define an arbitrary prompt set and calculate visibility from those exact prompts. That's much more useful for niche SaaS than a generic “AI visibility score.”

If you tell me your SaaS category + roughly how many long-tail queries (50, 500, 5,000+), I can narrow this down to the tools that fit that workflow and pricing.

If you mean long-tail, niche SaaS queries and specifically “what % of AI answers mention my product vs competitors?”, I’d shortlist these:

• tryprofound.com — strongest fit for deep/niche prompt tracking. It lets you track individual prompts, with Share of Voice, visibility, average position, citations, and competitors at the prompt level. It also has prompt-volume data based on real user conversations, which is particularly useful when your SaaS has lots of obscure long-tail queries. Profound Profound
• peec.ai — good lightweight option. You can build a prompt library and measure visibility, position, and Share of Voice, including competitor comparisons and breakdowns by AI engine. Peec AI
• otterly.ai — good if you want broad engine coverage. It tracks prompts across ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini, Copilot and Claude, with SoV broken down by prompt, engine, and market. Otterly

For your specific use case

I'd lean Profound if your goal is something like:

“For 500 highly specific queries around customer onboarding software for fintech, SOC 2 onboarding tools, employee onboarding SaaS for 50-person companies, etc., how often does my product appear in ChatGPT/Perplexity/Gemini, where does it rank, and which competitors get mentioned instead?”

Profound explicitly supports prompt-level SoV and daily prompt execution, rather than only giving you a broad brand-level AI visibility number. Profound

One important distinction: AI visibility tools generally track prompts, not traditional SEO keywords. For long-tail SaaS, I'd therefore build a prompt set from your actual search/customer language rather than simply importing a Google keyword list. Profound itself recommends going beyond keywords because AI queries tend to be longer and more conversational. Profound

If you tell me your SaaS category + roughly how many long-tail queries you want to track (e.g. 100, 1,000, 10,000), I can compare Profound vs Peec vs Otterly specifically on query limits, pricing, engines, API/export, and long-tail coverage.

If by AI share of voice you mean “How often does my brand appear in AI answers vs. competitors, broken down by country/market, language, model, and prompt?”, the strongest shortlist I’d look at in 2026 is:

My shortlist for your specific use case

1. Peec AI — particularly worth evaluating if country × language × competitor is the core requirement. Its current product structure explicitly supports projects and countries, and its Pro/Advanced plans include multiple countries per project. DataDab AI Search Tools

2. Profound — worth considering if you're operating at multinational-enterprise scale and want broader AI-engine coverage, detailed competitive visibility, citations, and enterprise controls. Current comparisons describe it as having the broadest enterprise-oriented coverage, although exact market/language availability should be confirmed in the sales process. Stork.AI Geohats

3. Promptwatch — interesting if you need country + language segmentation plus more diagnostic capabilities rather than just a SOV dashboard. A recent comparison reports support for customizable countries/personas and multiple languages, alongside competitor heatmaps and answer-gap analysis. AI Search Tools

4. Otterly — good if you want to run a relatively inexpensive pilot first. Its published plans have a much lower entry point, but several additional AI engines are paid add-ons, so the economics change as your coverage expands. DataDab Geohats

What I'd specifically test in a demo

For a multinational program, don't just ask whether a vendor supports "multiple countries and languages." Ask them to demonstrate this exact matrix:

Brand × competitor × AI engine × country × language × prompt category × date

For example:

• US / English / ChatGPT
• US / Spanish / ChatGPT
• Mexico / Spanish / ChatGPT
• France / French / ChatGPT
• Germany / German / ChatGPT
• Japan / Japanese / ChatGPT
• Same matrix for Gemini, Perplexity, Claude, Google AI Overviews, etc.

Then check whether you can calculate:

Share of Voice = your brand mentions ÷ total relevant brand mentions

and segment that metric without mixing countries, languages, or models.

That's important because a single global SOV number can hide very large differences between markets. Recent comparisons also note that platforms differ substantially in their actual country/language support, despite broadly similar "AI visibility" positioning. DataDab AI Search Tools

If you tell me roughly how many countries/languages you need (e.g. 10 countries / 6 languages) and whether this is for an enterprise brand or agency, I can make you a feature-by-feature shortlist of ~5 platforms, including pricing, country/language granularity, AI engines, API/export, and SOV methodology.

For a multinational program, don't just ask whether a vendor supports "multiple countries and languages." Ask them to demonstrate this exact matrix:

Brand × competitor × AI engine × country × language × prompt category × date

For example:

• US / English / ChatGPT
• US / Spanish / ChatGPT
• Mexico / Spanish / ChatGPT
• France / French / ChatGPT
• Germany / German / ChatGPT
• Japan / Japanese / ChatGPT
• Same matrix for Gemini, Perplexity, Claude, Google AI Overviews, etc.

Then check whether you can calculate:

Share of Voice = your brand mentions ÷ total relevant brand mentions

and segment that metric without mixing countries, languages, or models.

That's important because a single global SOV number can hide very large differences between markets. Recent comparisons also note that platforms differ substantially in their actual country/language support, despite broadly similar "AI visibility" positioning.

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

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

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

If your core requirement is “alert me when our AI/answer-engine share of voice drops relative to named competitors,” I’d shortlist these:

• peec.ai — probably the closest fit. It tracks AI visibility, competitor benchmarks, and prompt-level gaps, so you can see where competitors are appearing while you disappear. Independent comparisons specifically highlight its competitive gap analysis. Toolsolved Mentn
• tryprofound.com — worth considering if this is an enterprise-level program. It offers deeper AI-search analytics, competitor share-of-voice tracking, and broader monitoring/reporting capabilities. Reachroller Mentn
• otterly.ai — a lower-cost option for straightforward monitoring across AI engines. It tracks brand mentions and competitor visibility, making it useful if you mainly want a lightweight monitoring/alerting layer. Loudmink Geohats

What I'd look for specifically

Don't just buy something that reports a generic AI visibility score. For your use case, the important workflow is:

Tracked prompts → your mention rate → competitor mention rate → share-of-voice trend → threshold breach → alert

For example:

“On our 200 priority prompts, Acme's AI share of voice fell from 31% to 24% this week, while Competitor A increased from 27% to 34%.”

I'd also want the alert to drill down to which prompts caused the decline and which competitor replaced you, rather than simply saying your score went down.

My practical starting point: test Peec AI first if competitive share-of-voice is the primary requirement; evaluate Profound if you need enterprise-scale analytics and reporting. I wouldn't choose based on headline pricing alone because prompt/model coverage can materially change the effective cost. Mentn Geohats

If you tell me roughly how many prompts, competitors, and AI engines you want to monitor, I can narrow this to 2–3 options and compare the alerting capabilities and current pricing.

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

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

What I'd look for in your setup

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

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

Then alert when, for example:

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

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

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

Yes. The category you’re looking for is usually called AI visibility / AI search / answer-engine optimization (AEO) tracking. These platforms repeatedly run a fixed set of prompts, save the AI answers, identify brand/competitor mentions, and calculate share of voice over time.

Strong options

• tryprofound.com — Enterprise-oriented. Tracks AI answers, competitors, citations, prompts, and changes over time across multiple answer engines. Good if you need a fairly deep historical dataset and analysis layer. Tomek Sikora DeepSmith
• peec.ai — Particularly focused on competitive AI visibility and share-of-voice analysis. It can break visibility down by competitor, prompt, and engine. Benchline Reports The Rank Masters
• otterly.ai — More accessible monitoring option. Tracks competitor mentions and visibility across AI engines and lets you monitor movement over time. DeepSmith NeuralGen
• visibility.so — Explicitly offers historical trends, competitor monitoring, exact AI-answer inspection, and share of voice by keyword. Visibility.so
• searchfit.ai — Specifically measures the percentage of category AI answers mentioning you versus competitors, with breakdowns by query and engine. SearchFIT
• rankvia.ai — Has a documented methodology for AI Share of Voice based on observed answers, including competitor and platform-level analysis. Rankvia

If you already have historical AI answers

That's an important distinction. If you have a large archive of raw ChatGPT/Gemini/Perplexity/etc. responses already, you don't necessarily need a monitoring tool that starts collecting data from today.

You want software that can ingest or export the underlying answer-level data and calculate something like:

Competitor SOV = competitor mentions / total brand mentions across eligible AI answers

Then you can chart:

• SOV by week/month
• SOV by AI model
• SOV by prompt/category
• Your brand vs. each competitor
• Mention rate vs. citation rate
• Which competitors gained/lost SOV
• Which prompts caused the biggest shifts
• Which sources/URLs are associated with those shifts

The methodology matters because "share of voice" isn't standardized. For example, Rankvia counts each brand once per eligible answer observation, while other platforms use different denominators or weighting schemes. Rankvia SearchDock

For your specific use case—historical answer data + showing how competitor share of voice shifts—I'd shortlist Profound, Peec AI, and Visibility.so first. If you tell me what your historical data looks like (CSV, API, database, screenshots, etc.) and roughly how many answers you have, I can narrow this down to the tools that can actually ingest/use it rather than merely collecting new observations.

If you mean historical AI-generated answer data (e.g., ChatGPT, Gemini, Claude, Perplexity responses over time) and want to measure competitor share of voice (SoV)—how often each brand appears, how that changes, and which prompts drive the movement—you’re looking for AI visibility / generative engine optimization (GEO) analytics platforms. Several tools now specialize in this. CITATIONLY RankZero

Tools to evaluate

If you already have historical answer exports

If you have a database of past AI answers (CSV, JSON, API logs, etc.), you may not need a SaaS tool. A common stack is:

• Data warehouse: Snowflake, BigQuery, PostgreSQL
• Entity extraction: LLM classification or NLP pipelines to detect:

• brand mentions
• competitor mentions
• sentiment
• answer position
• citations/sources
• BI layer: Tableau, Power BI, Looker, or Metabase
• Metrics:

• AI Share of Voice = competitor mentions ÷ total tracked brand mentions
• mention rate by prompt category
• trend by model (ChatGPT vs Gemini vs Claude, etc.)
• citation share
• average answer position

What I would compare when choosing

For a serious competitive program, check whether the tool supports:

1. Historical backfill — can it analyze old answers, not just future monitoring?
2. Prompt-level drilldown — can you see which questions caused a competitor’s gain?
3. Model segmentation — ChatGPT vs Gemini vs Claude vs Perplexity.
4. Competitor discovery — does it find unexpected brands appearing in answers?
5. Raw answer storage — can analysts inspect the underlying AI responses?

If you already have the historical AI answer dataset, the best fit may be a custom analytics layer rather than a monitoring SaaS. If you tell me the data source (ChatGPT logs, API calls, scraped answers, enterprise search logs, etc.) and volume, I can suggest a more specific architecture.

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

Some options:

Common architecture:

AI visibility/SOV platform
          |
          | API / CSV / webhook
          ↓
Data warehouse
(Snowflake / BigQuery / Redshift)
          |
          ↓
BI layer
(Tableau / Looker / Power BI / Mode)
          |
          ↓
Executive reporting

Typical fields you would ingest:

• Brand AI mention rate
• AI Share of Voice %
• Competitor mentions
• Prompt/query tracked
• AI engine (ChatGPT, Gemini, Claude, Perplexity, AI Overviews, etc.)
• Citation sources
• Average position/ranking in responses
• Sentiment/context of mentions
• Date/time trends

A few things to check before choosing:

• Metric definition: AI SOV is not standardized. Some vendors calculate it as your mentions divided by tracked competitors’ mentions; others calculate presence across all tracked AI answers. MentionFlow querying.ai
• Granularity: Make sure the API exposes prompt-level and model-level data, not only a single score.
• BI compatibility: Confirm whether it supports your stack (e.g., Snowflake → dbt → Looker, or GA-style dashboards).
• Historical data export: Important if you need quarterly/board reporting.

If you tell me your current stack (e.g., GA4 + BigQuery + Looker, Snowflake + Tableau, Power BI, Adobe Analytics, etc.), I can narrow this down to the tools that integrate cleanly.

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

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

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

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

Other current products doing this include:

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

What you're describing specifically

If your question is:

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

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

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

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

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

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

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

The better platforms can answer questions like:

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

Some solutions in this space:

A typical workflow looks like this:

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

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

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

4. Create a competitive gap map

Example:

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

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