Answers that name a competitor

Yes. If you mean “what percentage of AI-generated answers mention/recommend my brand versus competitors?”, there are now dedicated AI-search visibility tools for exactly this.

Tools worth looking at

• semrush.com — Tracks AI Share of Voice and competitor visibility across platforms including ChatGPT, Perplexity, Gemini and Google AI surfaces. It can incorporate both mention frequency and position into its SOV measurement. Semrush
• support.birdeye.com — Particularly useful if you're already using Birdeye. It explicitly reports SOV versus competitors and supports ChatGPT, Gemini, Perplexity, Google AI Mode/Overviews, Grok and Claude. Birdeye Help Center
• searchable.com — Focuses heavily on brand visibility, SOV, sentiment, average position and competitor comparisons across a broad set of AI engines. Searchable
• seerly.app — Interesting if you specifically want live prompt testing rather than relying solely on a historical dataset. It tracks competitor SOV and breaks results down by engine and category. Seerly
• foglift.io — More focused specifically on AI SOV, with prompt-level evidence showing the answers, competitors and citations behind the score. Foglift

What I'd measure

Don't reduce this to one number. A useful AI Share of Voice dashboard would have:

The important methodological point is to use a fixed set of buyer-relevant prompts and the same competitor set over time. Otherwise your SOV can move simply because the questions or denominator changed. Foglift theStacc

If you're choosing one tool: I'd start by comparing Semrush, Searchable, Seerly, and Foglift based on whether you want an established broader SEO/AIO suite, a broad AI-visibility dashboard, live prompting, or dedicated SOV analytics.

If you tell me your industry + 3–5 competitors + whether you're B2B or B2C, I can narrow this down to the tools that are most appropriate for your use case and pricing level.

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 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. There are now several platforms specifically built to track AI share of voice (SoV) over time—i.e., how often your brand is mentioned/recommended relative to competitors across ChatGPT, Gemini, Perplexity, Google AI, etc.

The main options

• Semrush Enterprise AIO — Tracks AI share of voice against competitors and shows how it changes over a selected period. It also breaks performance down by AI platform. Semrush
• Seerly — Focuses heavily on AI visibility and SoV, with competitor/category tracking and trend charts over weeks and months. Seerly
• Zumi — Tracks mention rate, SoV, average position, and competitor performance, with daily refreshes across multiple AI engines. Zumi
• DemandSphere — Enterprise-oriented monitoring across ChatGPT, Gemini, Perplexity, AI Overviews, Copilot, etc., including historical trends and competitive SoV. DemandSphere DemandSphere
• LLMrefs — Tracks prompts daily and charts AI visibility/share of voice over time, including competitor comparisons. LLMrefs
• Whaily — More focused on benchmarking AI visibility by category, with recurring buyer-intent queries across ChatGPT, Gemini, and Perplexity. Whaily
• Ahrefs AI Visibility Index — Interesting if you want industry-wide benchmarking at scale rather than only tracking your own prompt set. It says its index is based on 459M+ modeled prompts and is updated monthly. Ahrefs

There's also a free HubSpot AI Search Sensor that shows broader industry-level trends in AI visibility, citations, volatility, and AI-referred traffic. It doesn't track your individual brand, though. HubSpot

If what you mean is specifically…

"I want to see a graph showing my industry's AI share of voice changing month by month, with my company + competitors on the same graph."

I'd look particularly closely at Semrush, Seerly, DemandSphere, and LLMrefs. They are designed around exactly that kind of longitudinal competitive tracking rather than just giving you a one-off "AI visibility score."

The important distinction is how they construct SoV. Some calculate it from a fixed/custom prompt set, while others use large modeled/query datasets. For your own marketing reporting, a fixed set of high-intent industry prompts rerun consistently is generally much easier to interpret than a mysterious composite score.

If you tell me your industry + 3–5 competitors, I can compare the available platforms specifically for that use case—including which ones actually show historical industry trends, which AI engines they cover, approximate pricing, and whether you can export the data.

Yes. There are several platforms now that track AI Share of Voice (AI SOV) — essentially, how often your brand appears in AI-generated answers compared with competitors, and how that changes over time. The category is often also called AI visibility, LLM visibility, or Generative Engine Optimization (GEO) analytics. Siftly Semrush

Some options:

A typical dashboard will let you answer questions like:

• “When someone asks AI ‘best software for X industry’, how often are we mentioned?”
• “Are competitors gaining share in ChatGPT answers?”
• “Which prompts/categories are we losing?”
• “Did our content or PR campaigns increase AI visibility?”
• “How does our AI presence compare with our traditional SEO market position?”

A few things to check before choosing:

• Prompt coverage: Does it let you define the actual buyer questions in your industry, or only generic prompts?
• Competitor tracking: Can you add your real competitive set?
• Model coverage: Does it include the AI surfaces your customers use (ChatGPT, Claude, Gemini, Perplexity, Google AI results)?
• Historical baseline: Many tools only start collecting data after signup, so “trends” may not mean years of historical data. Presenc AI

If you tell me your industry (e.g., SaaS, healthcare, finance, ecommerce, agency, travel) and whether you care more about competitor benchmarking or measuring your own GEO efforts, I can narrow this down.

If your goal is to monitor how often competitors appear in AI assistant answers (ChatGPT, Gemini, Claude, Perplexity, etc.) and calculate percentage share, you’re looking for an AI visibility / Generative Engine Optimization (GEO) monitoring platform, not traditional SEO rank tracking. These tools run a set of buyer-relevant prompts, capture AI answers, detect brand mentions, and calculate competitive share of voice. Semrush Search Engine Land

Recommended approach

1. Use an AI answer monitoring platform

Shortlist:

2. Define your measurement framework

I would track these metrics:

AI Share of Voice (primary metric)

Formula:

Competitor AI Share % =
(Number of tracked answers where competitor appears)
/
(Total tracked answers)
× 100

Example:

You monitor 1,000 prompts:

• Your brand appears in 420 answers → 42%
• Competitor A appears in 610 answers → 61%
• Competitor B appears in 280 answers → 28%

Dashboard:

Add weighting so every mention is not equal

A brand appearing as the first recommendation should count more than a passing mention.

Example weighted score:

AI Visibility Score =
Mention frequency × Position weight × Sentiment weight

Possible weights:

• First recommendation: 3x
• Included in shortlist: 2x
• Mentioned casually: 1x
• Negative/inaccurate mention: adjust downward

3. Build the prompt universe

Do not only track your company name. Track the questions buyers actually ask:

Category prompts

• "Best [category] software"
• "Top [category] vendors"
• "Alternatives to [competitor]"
• "Compare [brand A] vs [brand B]"
• "Who are the leaders in [market]?"

Use-case prompts

• "Best tool for enterprise teams"
• "Best option for startups"
• "Most affordable solution"

Geographic prompts

• "Best [service] providers in [region]"

A typical program might monitor:

• 200–1,000 prompts
• Weekly or daily runs
• Multiple AI engines
• 5–15 competitors

4. Track the reasons behind competitor wins

The most useful output is not just:

"Competitor X appears 15% more often."

You want:

• Which prompts they win
• Which sources AI assistants cite
• Which comparison pages influence answers
• Whether they appear earlier in responses
• Whether they are recommended more often

Several AI visibility platforms now include citation/source analysis because AI answers often depend on the sources models retrieve or rely on. MentionScout Prism

Suggested implementation

For an enterprise program:

Data collection

• Profound or Scrunch AI
• Daily/weekly prompt runs
• Capture raw answers + citations

Analytics layer

• Export to:

• Tableau
• Power BI
• Looker

Executive dashboard
Track:

• AI Share of Voice %
• Competitor gap
• Prompt categories won/lost
• Citation share
• First-position recommendation rate
• Trend over time

A practical starting point would be 100–200 high-value prompts, 10 competitors, weekly measurement for 90 days. That gives enough signal to see whether your brand presence in AI answers is improving or declining without creating excessive monitoring noise.

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 you want to measure AI share of voice (AI SOV) before and after content updates, look for tools that can do three things:

1. Track a fixed set of prompts over time (e.g., “best CRM for startups”).
2. Measure brand mentions/citations in AI answers.
3. Correlate changes with content updates, refreshes, or new pages.

Some strong options:

For your specific use case (content update → AI SOV change)

I’d prioritize tools with:

• Prompt cohorts: Save 50–500 target prompts and rerun them after every content release.
• Change annotations: Ability to mark “updated pricing page,” “published comparison article,” etc.
• Citation tracking: Did AI models start citing the updated page?
• Competitor baseline: Did your share rise because you improved, or because competitors declined?
• Platform splits: ChatGPT vs Perplexity vs Gemini vs AI Overviews.

A practical measurement setup:

1. Pick 100–200 buyer-intent prompts.
2. Record baseline:

• brand mention rate
• citation rate
• competitor mentions
• average position in answers
• Publish/update content.
• Recheck after ~2–8 weeks.
• Segment results:

• prompts directly targeted by the update
• unrelated control prompts
• AI engines affected

If you want causal measurement (not just “SOV went up”), look at platforms that support experimentation or change tracking. Some newer AI visibility platforms are adding workflows that connect CMS/content changes to prompt-level SOV movement rather than only reporting a dashboard number. Sill

For most content teams, I’d shortlist:

• Small/mid-size team: Otterly.AI or Scrunch
• SEO-heavy organization: Semrush + AI visibility tooling
• Enterprise GEO program: Profound or similar enterprise platforms

Scrunch HubSpot Blog

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.

Yes. There are now several platforms specifically targeting agencies that want to sell AI visibility / GEO reporting as a recurring client service, including white-label dashboards and reports.

A few worth looking at:

• geneo.app — Probably closest to the classic “white-label SaaS portal” model. It offers your own domain, logo, client portal, Share of Voice, mentions, citations, and tracking across ChatGPT, Gemini, Google AI Overviews, and Perplexity. Geneo
• reachllm.com — More comprehensive agency infrastructure: client workspaces, Share of Voice, citation rate, average rank, sentiment, source gaps, execution tracking, and white-label dashboards. ReachLLM
• rankfender.com — Strong if the priority is automated reporting. It has white-label reports, client portals, competitive SOV, per-AI-system breakdowns, scheduled delivery, and PDF export. RankFender
• agencyscope.ai — More report-centric. Tracks recurring prompts across ChatGPT, Perplexity, and Gemini and produces white-label client reports with competitor SOV and prompt-level evidence. AgencyScope AgencyScope
• pondral.com — Interesting if you care about traceability. It records the underlying AI responses, offers client share links, white-label reports/portals, API/webhooks, and exports. Pondral
• asvaai.com — Agency-focused AEO platform with multi-client tracking, SOV benchmarking, citation monitoring, and white-label dashboards/reports. Asva AI
• llmpulse.ai — Specifically positions itself as a white-label GEO/AEO platform for agencies, including multi-client dashboards and reporting integrations. LLM Pulse

One important distinction

There are really three different products emerging here:

If what you're envisioning is “Semrush/AgencyAnalytics, but specifically for AI Share of Voice, with each client getting a branded portal”, then I'd focus on Geneo, ReachLLM, Rankfender, and Pondral rather than tools that only generate PDF reports.

There is also AgencyAnalytics' AI Tracker, which is interesting if you don't necessarily need a completely separate GEO platform: it puts AI visibility alongside the traditional SEO/traffic reporting agencies already use, and tracks SOV across ChatGPT, Google AI Overviews/AI Mode, Claude, Perplexity, and Gemini. agencyanalytics.com

If you're thinking about building a product in this space, I can also map out what these platforms are missing, their pricing/business models, and where there may still be a compelling wedge for a new agency-focused AI SOV platform.

Yes. There are now several platforms specifically aimed at agencies selling AI visibility / GEO (Generative Engine Optimization) services with multi-client dashboards, AI Share of Voice tracking, and white-label reporting. The category is still emerging, but these are the closest fits:

A few things to watch when evaluating them:

Metrics that matter for client reporting

A useful agency dashboard usually includes:

• AI Share of Voice (SoV)

% of tracked AI answers where the client appears vs competitors

• Mention rate

• “How often is the brand named?”
• Position/rank

• Whether the brand is recommended first, second, etc.
• Citation footprint

• Which websites AI engines rely on when mentioning the brand
• Sentiment/context

• Whether mentions are positive, neutral, or negative
• Prompt coverage

• Which buyer questions are being tracked
• Trend reporting

• Month-over-month movement

If you are building an agency offer

The closest analog is probably an SEO reporting stack (Semrush Agency, AgencyAnalytics, Looker Studio) but for AI answers. The winning workflow tends to be:

1. Add client + competitors
2. Define 50–200 buyer prompts
3. Track ChatGPT / Gemini / Claude / Perplexity responses
4. Calculate SoV and citation gaps
5. Deliver monthly branded report
6. Tie improvements to content, PR, and technical changes

If your requirement is specifically “clients log into reports.youragency.com and never see the underlying vendor”, prioritize platforms that offer true client portals (for example Geneo, ReachLLM, Rankfender). If PDF/deck white-labeling is enough, there are more options. Geneo ReachLLM RankFender

I can also put together a comparison by price, API availability, number of clients supported, and whether they allow full SaaS resale/true white-labeling if you're evaluating this as an agency product.

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.

If your core requirement is AI share of voice (SOV) across countries and languages, I’d shortlist these:

• tryprofound.com — Strong fit for enterprise/global competitive intelligence. Its current dataset spans 1.9B+ real-user conversations, 50+ industries, and multiple answer engines; its 2026 research specifically examines cross-market visibility. Profound
• enterprise.semrush.com — Particularly compelling if you already use Semrush. It advertises 15 regional AI-search databases, 289M prompts, competitor benchmarking, and drill-downs from global SOV to market/city/product/prompt. Semrush for Enterprise
• llmpulse.ai — Very explicitly designed for country + language comparisons. It supports multiple countries/languages in the same project, portfolio-level SOV, and BI/API integrations including Power BI and Looker Studio. LLM Pulse
• demandsphere.com — Interesting if you want to combine traditional search SOV and AI SOV. It covers 200+ traditional-search markets plus LLM visibility, with mentions, citations, sentiment and SOV. DemandSphere DemandSphere
• seorce.com — Built around multinational brands, with native-language monitoring across 14 languages, regional competitor SOV, and a consolidated HQ view. Seorce
• searchable.com — Enterprise-oriented tracking across models, markets and prompts, including SOV/citation share and competitor benchmarking. Searchable

How I'd narrow it

One important caveat: “share of voice” isn't standardized across vendors. Different platforms can weight mentions, position, citations, prompts, models, or response frequency differently. Verity Score

For a serious global measurement program, I'd therefore evaluate vendors on methodology before dashboard quality: can they run genuinely localized prompts, rather than merely translating a US prompt? Can you hold the prompt set constant across markets? Can you compare brand mention SOV vs citation SOV separately? And can you export the underlying response/citation data?

If you tell me your number of markets/languages, industry, and approximate monthly prompt volume, I can narrow this to a 3-vendor shortlist and compare coverage, methodology, integrations, and likely pricing.

Yes. This is emerging as a distinct GEO / AI-search intelligence category, and a few platforms now combine competitive AI Share of Voice with citation-level analysis.

The closest matches I found:

• tryprofound.com — probably the most established enterprise-style option. It tracks Share of Voice, visibility rank, citation share, sentiment, and average position, with competitor benchmarking across ChatGPT, Perplexity, Google AI Overviews, Gemini, Copilot, Grok, etc. Profound
• zumihq.com — particularly close to your wording. Its visibility model explicitly combines mention frequency + competitor SOV + position + the pages that AI cites, with "citation quality" influencing visibility. Zumi
• ranksage.com — interesting if by citation quality you mean evaluating whether the cited page actually supports the claim. It combines AI Share of Voice with Citation Integrity, checking whether cited URLs are live and whether they actually mention/support the brand. RankSage RankSage
• citationly.ai — combines competitor SOV with source-level citation analysis and explains which sources are driving competitor visibility. CITATIONLY CITATIONLY
• geoly.ai — has a competitor × AI-model matrix showing SOV, citation rate, visibility and rankings side-by-side. GEOly
• bluejar.ai — combines AI SOV/competitor benchmarking with a mapping of the citation sources that could be improved. BlueJar

The interesting gap

There is an important distinction between "citation tracking" and "citation quality scoring."

Most platforms can tell you:

"ChatGPT cited Competitor A 38% of the time and you 14%."

Far fewer appear to answer:

"Competitor A's citations are higher quality than yours because their sources are more authoritative, more directly relevant to the claim, more recent, more frequently cited, and better positioned in the answer."

RankSage's Citation Integrity gets closer to the latter, while Zumi explicitly incorporates citation quality into its visibility model. RankSage Zumi

So if you're thinking about this as a product concept, I would frame the missing layer as something like:

AI Share of Voice → Citation Share → Citation Quality → Competitive Citation Gap

For example:

That combination is considerably more actionable than a generic "AI visibility score."

If you're asking because you're evaluating whether to build/buy a platform, I can also map the current vendors across SOV, citation quality, source authority, competitor attribution, prompt-level data, and pricing and identify what appears genuinely underserved.

Yes. A few platforms now combine AI Share of Voice + competitor benchmarking + citation-level analysis:

• tryprofound.com — strongest enterprise option; tracks citation share, SOV, competitor visibility, position, sentiment, and the specific pages competitors are getting cited from. Profound
• citationly.ai — particularly close to your description: competitor SOV plus citation tracking and source-level diagnostics. CITATIONLY CITATIONLY
• geoly.ai — explicitly puts SOV + Citation Rate vs. competitors side-by-side across AI models. geoly.ai
• sona.com — SOV plus a deeper “citation authority” layer showing which source types/domains drive visibility. sona.com

If by “citation quality scoring” you mean a score for how authoritative/trustworthy the cited source is, rather than simply citation frequency, that appears to be a less-served gap. Most platforms measure citation rate/share rather than a rigorous citation-quality score.

If you're evaluating this as a product opportunity, that distinction is interesting.

If you mean AI share of voice as in “how often ChatGPT, Gemini, Perplexity, etc. mention/recommend us vs. competitors,” you’re looking for an AI visibility monitoring / AEO (answer engine optimization) tool. These are the ones I’d shortlist:

My pick by use case

• Enterprise / executive reporting: Profound or MentionScout. MentionScout
• SEO/content team trying to win AI answers: MentionScout or RankJag, because they expose the prompts where competitors appear and you don’t. MentionScout RankJag
• Startup/lean marketing team: Spottlo or CompeteScan for lighter-weight monitoring. CompeteScan Spottlo

When evaluating, I’d make sure it has:

• Scheduled runs (daily/weekly), not one-off audits
• Tracking across the AI engines your buyers actually use
• Competitor comparison on the same prompt set
• Alerts when:

• your mention rate falls X%
• a competitor enters your top prompts
• citations shift from your domains to theirs
• your rank/position changes

A good setup is usually:

1. Define 50–200 buyer prompts (“best X,” “X alternatives,” “X vs Y”).
2. Add 3–10 competitors.
3. Set a threshold alert (for example, “notify when our AI share of voice drops 10% week over week”).
4. Review the exact prompts and sources causing the decline.

If you tell me your industry, number of competitors, and whether you care more about ChatGPT vs Google AI Overviews vs Perplexity, I can narrow this to 2–3 options.

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.

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 several tools now that expose AI/LLM share-of-voice metrics through APIs or BI connectors, so you can add them to an existing reporting stack rather than adopting another dashboard as the system of record.

A few worth looking at:

• Meltwater AI Visibility — has an API for AI visibility, including share of voice, trends, mentions, sentiment, and citations. This is particularly interesting if you already use Meltwater for social/listening analytics. Meltwater Developer Portal
• DemandSphere — explicitly positions its platform as a unified SERP + LLM + GSC dataset, with REST APIs and connections to BigQuery, Snowflake, Looker Studio, Tableau, Power BI, S3, etc. This is a strong fit if your reporting already runs through a warehouse/BI layer. DemandSphere
• Pi Datametrics — provides an SEO API covering both traditional and AI search data, with an option called Pi Sync that automatically transfers selected data into a SQL database. Pi Datametrics
• Prominence AI — offers a REST API specifically for LLM visibility metrics, including share-of-answer data across AI engines, intended for feeding dashboards and automated reporting. Prominence AI
• Signal Sumo — has an API endpoint specifically for AI-visibility share of voice, including the metric breakdown from its scans. SignalSumo
• GetIntel / LLM Pulse — more BI-oriented options if your existing reporting is Looker Studio. Both expose AI visibility metrics such as SOV, mentions, citations and competitor data directly in Looker Studio. GetIntel LLM Pulse

If you already have a data warehouse

I'd focus on tools with a REST API + scheduled ingestion, rather than a standalone reporting product. Your architecture could simply be:

AI visibility provider → API → Snowflake/BigQuery → existing semantic layer → Tableau/Looker/Power BI

That lets you combine AI SOV with things like organic search, GA4, CRM/revenue and traditional brand SOV in the same reporting model.

If you tell me what your current stack is — e.g. Snowflake + Looker, BigQuery + Tableau, Power BI, HubSpot, etc. — I can narrow this to the tools that have the cleanest integration and compare their AI engines, SOV methodology, API/warehouse connectivity, and reporting capabilities.

Yes. There are now several tools specifically designed to pull AI/LLM visibility or “share of voice” metrics into an existing reporting stack, usually through an API, CSV export, or BI connector.

A few worth looking at:

• sovtracker.com — REST API specifically designed to feed AI share-of-voice data into your own tools. It tracks ChatGPT, Google AI Overviews, Gemini, Perplexity, and Claude on paid plans. SovTracker SovTracker
• aipeekaboo.com — particularly relevant if your reporting stack is Looker Studio: it offers a Looker Studio connector plus REST API and CSV export. Peekaboo
• signalsumo.com — exposes AI-visibility/share-of-voice metrics through an API, including competitive SOV and scan-level metrics. SignalSumo
• sellm.io — API-based analysis with SOV, coverage, average position, sentiment, and provider-level breakdowns. Sellm
• ospea.io — more reporting/operations oriented; its API exposes weekly AI-visibility SOV and a combined report joining AI scans with Google Search Console and Bing AI Performance data. OSPEA

If you're trying to plug this into an existing analytics stack

I'd prioritize the API-first options rather than another standalone dashboard. The architecture would typically be:

AI visibility platform → API → warehouse → Looker/Tableau/Power BI → existing marketing report

That lets you combine AI SOV with things like organic traffic, conversions, branded search, GSC data, and revenue rather than creating a separate AI-visibility report.

One important distinction: make sure the vendor defines SOV clearly. Some calculate it as your mentions ÷ all tracked-brand mentions, while others include every brand mentioned by the models in the denominator. Those can produce materially different numbers. Senso.ai Sellm

If you tell me what your existing stack is (e.g. GA4 + BigQuery + Looker, HubSpot + Tableau, Snowflake + Power BI, etc.), I can narrow this to the tools that integrate most cleanly and show you what the reporting architecture would look like.

Yes. There’s now a fairly distinct category of tools for this: AI search / GEO / AEO visibility monitoring.

They do more than traditional SEO rank tracking. They repeatedly ask buyer-intent questions across AI engines and show which competitors are being mentioned, recommended, and cited—and which content/sources are behind those answers.

A few examples I found:

• citepower.com — tracks ChatGPT, Perplexity, Gemini, Claude and Google AI Overviews, including competitor share of voice, cited sources, and the full AI answers. CitePower
• citationradar.ai — specifically surfaces the questions where competitors beat you, the pages driving their citations, and citation/content gaps. Citation Radar
• rankzero.io — focuses on competitive AI visibility: which brands appear instead of you, on which prompts, and which sources cite your competitors but not you. RankZero
• rivalsweeper.com — includes a citation-source overlap view intended to identify the competitor content the LLM is drawing from. RivalSweeper
• geoecho.ai — monitors multiple AI engines and reports mention rate, share of voice, recommendation order and cited domains. GeoEcho

The distinction I'd make

If what you're asking is:

"Which competitor content is actually causing them to win AI answers, and therefore where are we losing share of voice?"

then you want something beyond an AI visibility score.

Ideally, the system should give you a chain like:

Buyer prompt → AI answer → competitors mentioned → citations → competitor URL → specific content/fact being used → your corresponding content gap

For example:

Prompt: "Best enterprise project-management platforms for distributed teams?"

ChatGPT mentions: Competitor A, Competitor B, You

Competitor A wins because it cites:

• /enterprise-guide
• G2 reviews
• a comparison article
• a third-party industry report

Your site is cited 0 times.

Opportunity: Your enterprise comparison page doesn't address X, Y and Z facts that appear repeatedly in the sources AI uses.

That's much more actionable than simply saying "your AI visibility score is 23."

There are products getting toward exactly this. For example, Citation Radar explicitly says it identifies the pages driving competitor citations, while RivalSweeper describes a citation-source overlap view for seeing which competitor content LLMs are drawing from. Citation Radar RivalSweeper

If you're evaluating this as a product/category to buy, I can also map out the current landscape—Semrush/Ahrefs-type incumbents vs dedicated GEO platforms—and tell you which ones actually expose competitor URLs/content rather than just giving you an AI visibility score.

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”.