Neutral mentions of 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.

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.

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 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. If your specific goal is “I changed a page → did my AI share of voice/citation rate increase?”, I’d shortlist these:

• peec.ai — best overall for straightforward measurement. It tracks AI visibility, average position, sentiment, and share of voice versus competitors, with prompt-level trends across AI engines. Peec AI
• tryprofound.com — best for enterprise/deep analysis. Stronger if you need granular prompt, citation, source, and competitive reporting rather than just a headline visibility score. Tomek Sikora DeepSmith
• otterly.ai — best budget option. Good for establishing a baseline and watching whether mentions/citations move after an update, but less useful for attributing why they moved. 1001 SEO Media NeuralGen
• Promptwatch — worth considering if attribution is the priority. It is specifically positioned around closing the loop from content changes to subsequent AI visibility/citation changes, rather than simply monitoring brand mentions. AI Search Tools
• scrunchai.com — enterprise option if you also care about crawler/traffic diagnostics. It is more appropriate when you want to understand whether AI systems are discovering and using your updated content, not just whether your brand appears. AI Search Tools Bloomiro

What I'd use

For a content/SEO team, I'd probably start with Peec AI. The measurement setup I'd want is:

1. Freeze a set of important customer prompts.
2. Record share of voice + citation rate + position for your brand and competitors.
3. Timestamp the content change.
4. Continue running the same prompts after the update.
5. Compare pre/post performance at the prompt and URL level, not merely your overall AI visibility score.
6. Ideally separate the effect of the update from normal model/competitor fluctuations.

One important caveat: most AI visibility tools are better at showing that your visibility changed than proving that your particular content update caused the change. Independent comparisons specifically flag page-level attribution and crawl detection as the weak points of many platforms. AI Search Tools

If you tell me your approximate budget and whether you're measuring ChatGPT, Perplexity, Gemini, Google AI Overviews, or all of them, I can narrow this to the 2–3 best tools and compare their exact before/after capabilities.

For long-tail SaaS keywords, I’d look first at tryprofound.com.

• Profound — particularly well suited if you want to build a large set of niche prompts and measure Share of Voice, visibility, rank, and citations at the individual-prompt level. You can upload your own prompts, track them daily, and segment by topic, platform, region, persona, etc. Profound Profound Help Center
• Its Prompt Volumes / Prompt Research Reports are especially relevant for long-tail discovery: Profound says it uses billions of real AI conversations to identify the actual questions users ask, including sub-intents and lower-volume prompts, which you can then add to tracking. Profound
• peec.ai is another option if your primary requirement is straightforward daily AI visibility/SOV monitoring across competitors and engines. It reports visibility, position, sentiment, and Share of Voice by engine. Peec AI

For your specific use case

If you have something like:

“best SOC 2 compliance software for a 20-person fintech”
“alternatives to [niche SaaS] for healthcare startups”
“AP automation software that integrates with NetSuite for SMBs”

I'd favor Profound, because you can feed it your exact long-tail prompts rather than relying exclusively on generic keyword tracking, and then see which competitors get mentioned, your SOV, position, and the sources AI cites. Profound Profound Help Center

If you tell me your SaaS category + ~10 example keywords, I can also compare Profound, Peec, Otterly, Scrunch, and similar tools specifically on long-tail coverage, prompt volume, SOV granularity, API/export, and price.

If your goal is specifically tracking share of voice for lots of niche, long-tail SaaS queries inside AI-generated answers, I’d shortlist these:

• otterly.ai — best fit for long-tail prompt tracking on a budget. You can define individual search prompts, monitor them repeatedly, and see brand coverage/share of voice, competitors, rankings, sentiment, and citations. It explicitly supports long-tail questions. Otterly Semrush
• peec.ai — best for SaaS competitive benchmarking. Particularly useful if the key question is “For these 200 buyer-intent prompts, how often do I appear versus Gong/HubSpot/Intercom/etc.?” It emphasizes competitor benchmarking and share-of-voice analytics. Arbling DeepSmith
• tryprofound.com — best for deep/enterprise analysis. More appropriate if you need granular prompt, citation, source, and answer-level analytics rather than simply a lightweight rank tracker. Tomek Sikora DeepSmith
• semrush.com — worth considering if you already use Semrush. Its AI Visibility tooling calculates AI share of voice and competitor visibility, although I'd favor the dedicated tools above if your primary use case is a large custom library of niche prompts. Semrush

My pick for your use case

For a niche B2B SaaS, I'd start with Otterly or Peec.

The important distinction is that you don't really want an AI “keyword rank tracker.” You want a prompt-level panel where you can maintain queries such as:

• “best [category] software for 50-person companies”
• “[category] alternatives to [competitor]”
• “best [category] for [specific industry]”
• “how to [job-to-be-done] without [competitor/category]”
• “[competitor] vs [your product]”
• “cheapest [category] with [specific feature]”

…and then calculate something like:

Your AI SOV = your mentions / all tracked brand mentions × 100

Ideally, also track position/rank within the answer, citation share, competitor presence, and SOV by individual prompt, not just one aggregate visibility score. Otterly specifically exposes SOV by prompt, engine, and market. Otterly

If you tell me your SaaS category + roughly how many long-tail queries you want to track (50 / 500 / 5,000+), I can narrow this down to the best tool and plan.

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’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