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Best AI Visibility Platforms for Tracking Recommendation Share

Profound is the consensus leader for tracking AI recommendation share, named by 6 of 7 platforms with an average listed position of 1.67 and a best position of 1.

Research: 2026-09-197 usable platform responsesRead the methodology ↗

Answer Capsule

Profound is the consensus leader for tracking AI recommendation share, named by 6 of 7 platforms with an average listed position of 1.67 and a best position of 1. Peec AI is the strongest alternative for teams that want prompt-level share-of-voice and citation reporting at a lower entry price, while Semrush and SE Ranking fit buyers who want AI recommendation tracking folded into an existing SEO workflow. This study covered 7 AI platforms (openai, anthropic, deepseek, grok, perplexity, kimi, google) using one standardized prompt sent once to each. Of 34 unique entities named, 10 qualified by being named by at least two platforms. The principal limitation is that platform mentions count only ranking-discovery mentions, not fit-assessment completions, and every capability, price, and plan detail below is platform-reported evidence that was not independently verified.

Research Snapshot

  • Topic: Best AI visibility and LLM monitoring platforms for tracking recommendation share
  • Target buyer: Companies seeking AI visibility platforms for tracking recommendation share across AI search, generative-answer, and recommendation platforms
  • Use case: Recommendation-level data rather than simple brand mentions, with platform-by-platform results, recommendation position, historical trends, and competitor comparisons
  • Geography: United States
  • Platforms included (7): openai, anthropic, deepseek, grok, perplexity, kimi, google
  • Research date: 2026-09-19 (authoritative run date; platform-reported dates are provenance metadata and do not independently prove freshness)
  • Unique entities named: 34
  • Qualifying entities: 10
  • Eligibility rule: Named by at least two platforms during ranking discovery
  • Ranking rule: Platform mentions, then average listed rank, then best listed rank

The Consensus Ranking

Questions This Section Answers

  • What are the best AI visibility platforms for tracking recommendation share in 2026?
  • Which AI visibility platform has the most cross-platform consensus for recommendation-level tracking?
  • How many of the 7 AI platforms named each of the top AI visibility tools for recommendation share?

Platform mentions count only ranking-discovery mentions. Several entities below were evaluated for fit by more platforms than named them during ranking discovery, and that gap is not a quality signal.

RankEntityPlatform mentionsAverage listed positionBest positionBest considered for
1Profound61.671Mid-market and enterprise teams monitoring branded and unbranded recommendation prompts across ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini, and other supported engines.; Buyers prioritizing competitor comparisons, platform-level reporting, citation analysis, average position, and trend reporting.; Teams that can accept prompt-sampling methodology and potentially custom enterprise pricing.
2Peec AI53.001Marketing, SEO, and content teams tracking brand recommendations across ChatGPT, Google AI Mode, Google AI Overviews, Microsoft Copilot, Gemini, and other supported models.; Companies needing daily position, visibility, sentiment, share-of-voice, competitor, prompt, and source reporting.; E-commerce companies needing SKU-level AI Shopping visibility, product position, win rate, and competitor comparisons, subject to plan and catalog requirements.
3Semrush54.802Marketing and SEO teams already using Semrush that want AI visibility combined with conventional SEO data.; Competitive benchmarking across ChatGPT, Gemini, Google AI experiences, and selected other platforms.; Teams needing prompt-level monitoring, competitor gaps, historical trends, cited pages, and reporting.
4OtterlyAI55.604Marketing and SEO teams monitoring recommendation prompts across ChatGPT, Google AI Overviews, Perplexity, Microsoft Copilot, and optional Gemini, Claude, and Google AI Mode.; Companies needing competitor comparisons, recommendation position or order, share-of-voice trends, citation tracking, exports, and API or MCP access on higher plans.; Budget-conscious teams beginning with a limited prompt set and daily tracking.
5Scrunch AI45.753Mid-market and enterprise marketing, SEO, and brand teams tracking prompts across several generative-answer platforms; Buyers that need both measurement and optimization workflows, including content recommendations and AI-agent-oriented content delivery; Teams needing competitor visibility, response position, citation, sentiment, and share-of-voice analysis
6Ahrefs Brand Radar34.334Companies already using Ahrefs that want AI visibility added to an existing SEO workflow.; Teams measuring brand mentions, citations, estimated impressions, and competitor AI Share of Voice across major AI search platforms.; Buyers needing exact buyer-question monitoring through Custom Prompts.
7Writesonic AI Visibility38.007US-focused teams needing prompt-level AI visibility, share-of-voice, citation, trend, and competitor reporting combined with GEO optimization workflows.; Buyers that want monitoring and recommended remediation in one SEO/GEO platform, especially at Growth or Enterprise tiers.; Content and growth teams already using Writesonic for AI content generation who want integrated visibility monitoring
8SE Ranking24.003Companies tracking brand recommendations across ChatGPT, Google AI Overviews, Google AI Mode, Gemini, and Perplexity.; SEO, content, and marketing teams that want AI visibility data connected to conventional SEO workflows.; Agencies and multi-brand teams needing competitor benchmarking, reporting, API access, or expanded tracking capacity.
9AthenaHQ26.003Marketing and SEO/GEO teams tracking recommendation rate, share of voice, mentions, citations, sentiment, and competitors across major AI answer platforms.; Teams that want monitoring combined with content-gap analysis and prescriptive optimization recommendations.; Organizations needing executive dashboards, integrations, or broader multi-region and enterprise capabilities, subject to plan confirmation.
10Rankscale27.007Companies tracking whether their brand is recommended or listed for defined commercial and comparison prompts.; SEO, GEO, and AEO teams needing recurring monitoring across ChatGPT, Gemini, Perplexity, Claude, Google AI Overviews, Copilot, and other engines.; Agencies and multi-brand teams needing competitor comparisons, dashboards, exports, or API access on higher plans.

Which Option Is Best for Which Version of the Buyer Need?

Questions This Section Answers

  • Which AI visibility platform should a buyer choose for recommendation share if they need the broadest multi-engine coverage?
  • Is Profound or Peec AI better for tracking recommendation share when prompt-level position and citation detail matter?
  • Which AI visibility platform is best for an agency managing multiple client brands on a limited budget?
Buyer needBest-fit optionWhy, per platform-reported evidence
Broadest multi-engine recommendation monitoring with enterprise controlsProfoundNamed by 6 of 7 platforms; documents Share of Voice, Average Position, platform filters, competitor comparisons, and up to 10 engines on Enterprise
Prompt-level share of voice with unlimited seats and lower entry costPeec AIReports visibility, average position, sentiment, and share of voice daily against competitors, with unlimited user seats on paid plans
AI visibility layered onto an existing SEO stackSemrushAI Visibility Toolkit reports AI Visibility Score, Share of Voice, Mentions, Cited Pages, and competitor gaps inside the Semrush ecosystem
Budget-conscious start with a small prompt setOtterlyAILite at $29/month.

1. Profound

Questions This Section Answers

  • Is Profound worth it for tracking AI recommendation share, and what are its main drawbacks?
  • Does Profound report recommendation position separately from general brand visibility and Share of Voice?
  • Which AI engines does Profound's Growth plan cover versus its Enterprise plan?

Profound is the consensus leader for this use case, named by 6 of 7 platforms with an average listed position of 1.67 and a best position of 1. It is the only entity in this index that every naming platform placed in the top three. The Profound fit review covers the full evidence bundle.

Why it ranked here. Profound received the most ranking-discovery mentions and the strongest average position. Platforms consistently described it as purpose-built for answer-engine visibility rather than retrofitted SEO tooling, and its documented metrics map closely to the buyer's stated criteria.

Best suited for. Mid-market and enterprise teams monitoring branded and unbranded recommendation prompts across ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini, and other supported engines, and buyers prioritizing competitor comparisons, platform-level reporting, citation analysis, average position, and trend reporting.

Main strengths for the use case. Profound documents Share of Voice and Average Position metrics that directly support relative recommendation prominence analysis, plus platform filters, competitor comparisons, date-range comparisons, visibility trends, ranking views, and exports [1]. Underlying answer responses and citations allow reviewers to inspect why a competitor was recommended [3]. Anthropic-reported evidence describes visibility rank as actual position among cited competitors, platform-by-platform, with daily prompt tracking across covered engines [4]. Google-reported evidence describes citation links used by AI models, brand co-mention analysis, and mention position heatmaps [6].

Main limitations. Public materials do not clearly prove a dedicated recommendation-share or recommendation-win-rate metric separate from general visibility and share of voice [1]. Growth coverage appears narrower than the enterprise capability list and may omit engines important to the buyer [7]. Self-serve plans are billed annually only, creating a large upfront commitment [9]. One platform reported that no corroborating evidence for the product could be found in its search results, producing an uncertain fit rating [10]. .

2. Peec AI

Questions This Section Answers

  • Is Peec AI worth it for tracking recommendation share, and where does it fall short?
  • Is Peec AI or Profound better for recommendation share when unlimited seats and lower entry cost matter?
  • Which AI engines are included in Peec AI's base plans versus paid add-ons?

Peec AI ranked second, named by 5 of 7 platforms with an average listed position of 3.0 and a best position of 1. The Peec AI fit review covers the full evidence bundle.

Why it ranked here. Peec AI received strong positions from openai, anthropic, and deepseek, and it was the only entity besides Profound to receive a first-place ranking from any platform. Its documented metrics — visibility, average position, sentiment, share of voice, and citation share — map directly to the buyer's criteria.

Best suited for. Marketing, SEO, and content teams tracking brand recommendations across ChatGPT, Google AI Mode, Google AI Overviews, Microsoft Copilot, Gemini, and other supported models, plus e-commerce companies needing SKU-level AI Shopping visibility subject to plan and catalog requirements.

Main strengths for the use case. Peec AI reports visibility, average position, sentiment, and share of voice against competitors, with daily tracking [11]. It groups prompts by topic and exposes visibility, share of voice, sentiment, position, mention count, and "fanout" queries [12]. It tracks both used and cited sources broken down by domain and URL with citation frequency, sorted by source type [14]. All paid plans include unlimited user seats [16].

Main limitations. Anthropic-reported evidence states Peec AI does not break down recommendations by product, SKU, or price point, and does not offer built-in content execution or traffic attribution [17]. Self-serve plans limit buyers to three selected models, with additional engines historically priced as add-ons [19]. API access is Enterprise-only per one source [20]. Starter at 50 prompts fills quickly when tracking brand plus competitors plus intent variants [21].

Pricing or cost summary. Anthropic-reported pricing lists Starter at $95/month, Pro at $245/month, and Advanced at $495/month on monthly billing, with annual equivalents of €70, €180, and €360 per month [22]. Prompt counts scale at 50, 150, and 350 [24].

3. Semrush

Questions This Section Answers

  • Is Semrush worth it for tracking AI recommendation share, and what does its AI Visibility Toolkit not measure?
  • Which AI platforms does Semrush's AI Visibility Toolkit cover, and is Perplexity included in the same reports as ChatGPT and Gemini?
  • How much does Semrush's AI Visibility Toolkit cost per domain, and what do additional prompts and seats add?

Semrush ranked third, named by 5 of 7 platforms with an average listed position of 4.8 and a best position of 2. The Semrush fit review covers the full evidence bundle.

Why it ranked here. Semrush received a second-place ranking from perplexity and consistent good-fit ratings across most platforms. Its strength is integration: buyers already running SEO operations can add AI visibility without a new vendor.

Best suited for. Marketing and SEO teams already using Semrush that want AI visibility combined with conventional SEO data, competitive benchmarking across ChatGPT, Gemini, and Google AI experiences, and prompt-level monitoring with competitor gaps, historical trends, cited pages, and reporting.

Main strengths for the use case. The AI Visibility Toolkit reports AI Visibility Score, Share of Voice, Mentions, Cited Pages, sentiment, and competitor comparisons, with Prompt Tracking reporting visibility and Average Position [25]. Competitor Research compares a domain with up to four competitor domains and identifies competitor mentions, missing prompts, missing sources, and topic gaps [27]. Semrush states its AI analysis uses a prompt database exceeding 317 million prompts and responses, refreshed daily on a rolling basis, across 117 regional databases, captured from real requests rather than LLM APIs [28].

Main limitations. Recommendation share is not clearly documented as one standardized metric across platforms; buyers may need to interpret mentions, share of voice, visibility, and average position as related but non-identical measures [25]. Public documentation is inconsistent about whether Perplexity is included in the same datasets and reports as ChatGPT, Gemini, and Google AI experiences [29]. The base toolkit is limited to one Brand Performance domain and 25 tracked prompts [30]. Anthropic-reported evidence describes coverage limited to six regional databases and US English only [31]. .

4. OtterlyAI

Questions This Section Answers

  • Is OtterlyAI worth it for tracking recommendation share, and does it track recommendation position as a distinct metric?
  • Which AI engines are included in OtterlyAI's base plans, and what do Gemini, Claude, and Google AI Mode add-ons cost?
  • Is OtterlyAI or Peec AI better for a small team tracking recommendation share on a limited budget?

OtterlyAI ranked fourth, named by 5 of 7 platforms with an average listed position of 5.6 and a best position of 4. The OtterlyAI fit review covers the full evidence bundle.

Why it ranked here. OtterlyAI received consistent mid-table placements and one strong fit rating. Its combination of low entry price, daily tracking, and citation-level detail made it a recurring recommendation for budget-conscious buyers.

Best suited for. Marketing and SEO teams monitoring recommendation prompts across ChatGPT, Google AI Overviews, Perplexity, Microsoft Copilot, and optional Gemini, Claude, and Google AI Mode, plus companies needing competitor comparisons, recommendation position or order, share-of-voice trends, citation tracking, exports, and API or MCP access on higher plans.

Main strengths for the use case. OtterlyAI states it runs tracked prompts across supported engines, stores each answer, and scores which brands were named, in what order, and in what tone [33]. It reports share of voice by prompt, engine, and market, daily trend lines, average rank, competitive benchmarking, and gap analysis [33]. Citation tracking shows which URLs and domains get cited most often, including third-party content sources [34]. Setup is described as fast, with monitoring operational within about an hour [35].

Main limitations. Lite supports only 15 prompts and one workspace, which may be insufficient for meaningful category, competitor, geography, and funnel coverage [36]. Several relevant engines are paid add-ons, increasing total cost and complicating cross-platform comparisons [36]. Anthropic-reported evidence states the platform tracks citation frequency and brand visibility but does not explicitly separate recommendations as a distinct data category from general brand mentions [37]. Share of voice is prompt-set dependent and should be read as directional [38]. Cancellation and data-deletion documentation is not fully consistent about post-cancellation data availability [39]. .

5. Scrunch AI

Questions This Section Answers

  • Is Scrunch AI worth it for tracking recommendation share, and how does its placement measure differ from exact recommendation rank?
  • Which AI engines does Scrunch AI cover on its Core plan versus its Enterprise plan?
  • What does Scrunch AI cost per month, and is API access included on the Core plan?

Scrunch AI ranked fifth, named by 4 of 7 platforms with an average listed position of 5.75 and a best position of 3. The Scrunch AI fit review covers the full evidence bundle.

Why it ranked here. Scrunch AI received a third-place ranking from openai and strong fit ratings from google and grok. It is the only entity in this index that combines recommendation monitoring with an AI-agent content delivery layer.

Best suited for. Mid-market and enterprise marketing, SEO, and brand teams tracking prompts across several generative-answer platforms, and buyers that need both measurement and optimization workflows including content recommendations and AI-agent-oriented content delivery.

Main strengths for the use case. Scrunch states it tracks brand presence, share of voice, response position, sentiment, citations, and competitive presence, with a placement view categorizing a brand as top, middle, or bottom and showing average listing order [41]. It reports monitoring across platforms including ChatGPT, Perplexity, Gemini, and Claude, with competitor comparisons by platform, prompt, topic, persona, funnel stage, and time period [42]. Google-reported evidence describes tracking of exact response positions, sentiment, and whether the brand was formally cited with a link or merely named [44]. GA4 integration tracks named AI crawler bots and attributes AI referral traffic [45].

Main limitations. Public materials do not clearly establish a universally standardized recommendation-share metric separate from presence, share of voice, or position [41]. Top/middle/bottom placement is a coarse position measure and may not equal exact recommendation rank [41]. Pricing and plan entitlements are not transparently documented in reviewed official materials [46]. Anthropic-reported evidence states the Insights functionality remains in beta with limited prescriptive guidance, and the Agent Experience Platform remains in limited beta [47]. Prompt update frequency is unclear, with independent reviews suggesting weekly rather than daily refresh [49]. .

6. Ahrefs Brand Radar

Questions This Section Answers

  • Is Ahrefs Brand Radar worth it for tracking recommendation share, and does it distinguish a recommendation from a mention?
  • How much does Ahrefs Brand Radar cost in total, including the required base Ahrefs plan?
  • Which AI engines does Ahrefs Brand Radar cover, and does it track Claude or Grok?

Ahrefs Brand Radar ranked sixth, named by 3 of 7 platforms with an average listed position of 4.33 and a best position of 4. The Ahrefs Brand Radar fit review covers the full evidence bundle.

Why it ranked here. Ahrefs Brand Radar received a strong average position from the three platforms that named it, but fewer platforms named it overall. Its fit ratings were mixed across nearly every platform that assessed it.

Best suited for. Companies already using Ahrefs that want AI visibility added to an existing SEO workflow, teams measuring brand mentions, citations, estimated impressions, and competitor AI Share of Voice across major AI search platforms, and buyers needing exact buyer-question monitoring through Custom Prompts.

Main strengths for the use case. Ahrefs defines AI Share of Voice as the percentage of AI responses in a topic set that mention or cite a brand versus competitors [50]. Custom Prompts allows buyers to track exact questions with scheduled refreshes and platform selection [51]. Brand Radar supports benchmarking AI Share of Voice against competitors and identifying visibility gaps, cited pages, and cited domains [52]. Anthropic-reported evidence describes citation source tracking showing which URL the model pulled the mention from [53].

Main limitations. Public documentation emphasizes mentions, citations, impressions, and AI Share of Voice rather than recommendation position or ranked recommendation share [50]. Anthropic-reported evidence states Brand Radar counts a mention when a brand appears at least once in a response but does not systematically distinguish whether the mention was a primary recommendation, alternative, negative example, or minor reference [54]. Coverage does not include Claude, Meta AI, or Grok [56]. Independent reviews document accuracy gaps in ChatGPT and Perplexity tracking [57]. Brand Radar lacks white-label client reporting, limiting agency deployment [58]. .

7. Writesonic AI Visibility

Questions This Section Answers

  • Is Writesonic AI Visibility worth it for tracking recommendation share, and does it report recommendation position?
  • Which AI platforms are included in Writesonic's Starter, Basic, and Growth plans?
  • How much does Writesonic AI Visibility cost, and does the Action Center require an Enterprise plan?

Writesonic AI Visibility ranked seventh, named by 3 of 7 platforms with an average listed position of 8.0 and a best position of 7. The Writesonic AI Visibility fit review covers the full evidence bundle.

Why it ranked here. Writesonic received the weakest average listed position among entities named by three or more platforms. Its distinguishing feature is the Action Center, which converts monitoring findings into remediation tasks.

Best suited for. US-focused teams needing prompt-level AI visibility, share-of-voice, citation, trend, and competitor reporting combined with GEO optimization workflows, and buyers that want monitoring and recommended remediation in one SEO/GEO platform, especially at Growth or Enterprise tiers.

Main strengths for the use case. Writesonic documents prompt-level tracking and reports AI Visibility as the percentage of tracked AI answers mentioning the brand, plus Share of Voice relative to tracked competitors and Citation Share relative to competitor citations [59]. The GEO dashboard supports filtering by platform including ChatGPT, Perplexity, Gemini, Claude, and Copilot [59]. The Competitors view compares tracked brands on AI Visibility, Citation Share, and Share of Voice with period-over-period trend indicators [60]. Anthropic-reported evidence describes the Action Center surfacing missing prompts, citation opportunities, technical fixes, and content recommendations ranked by impact [61].

Main limitations. No reviewed official source clearly states that Writesonic reports the ordinal position of a brand recommendation inside an AI answer or a recommendation-ranking score [59]. Lower tiers cover only three listed AI platforms, with the broadest coverage on Enterprise and therefore custom-priced [62]. Prompt and answer quotas can constrain statistical coverage [62]. Anthropic-reported evidence notes that monitoring is secondary to content in product DNA, with tracking less sharp than purpose-built monitoring tools for citation depth and competitor forensics [63]. Independent testing noted discrepancies between reported visibility and manual queries [64]. .

8. SE Ranking

Questions This Section Answers

  • Is SE Ranking worth it for tracking recommendation share, and does its AI Search Toolkit report recommendation position?
  • Is SE Ranking or Semrush better for AI recommendation tracking when the buyer already runs an SEO suite?
  • How much does SE Ranking's AI Search Add-on cost on top of the Core or Growth plan?

SE Ranking ranked eighth, named by 2 of 7 platforms with an average listed position of 4.0 and a best position of 3. The SE Ranking fit review covers the full evidence bundle.

Why it ranked here. SE Ranking received the strongest average listed position of any entity named by only two platforms, including a third-place ranking from perplexity. Its eligibility came from meeting the two-platform minimum.

Best suited for. Companies tracking brand recommendations across ChatGPT, Google AI Overviews, Google AI Mode, Gemini, and Perplexity, SEO, content, and marketing teams that want AI visibility data connected to conventional SEO workflows, and agencies and multi-brand teams needing competitor benchmarking, reporting, API access, or expanded tracking capacity.

Main strengths for the use case. SE Visible reports whether brands are mentioned in AI answers, their visibility, share of voice, position, sentiment, and competitor presence [65]. The AI Results Tracker supports prompt-level review of answers, brand and link placement, citations, and movements over time [66]. Anthropic-reported evidence describes a Top 3 Share metric tracking what percentage of answers mention or cite the brand in top-3 positions, plus citation coverage and average ranking within generated responses [67]. SE Ranking states it collects real AI responses rather than simulations [69].

Main limitations. Coverage is limited to the documented five AI engines and does not establish coverage of all recommendation, shopping, social, or vertical AI platforms [65]. Recommendation share is inferred from visibility, share of voice, position, prompts, and competitor data; a separately defined recommendation-share methodology is not clearly documented [65]. Prompt and check allowances can become expensive at multi-brand or high-frequency scale because each prompt-engine combination consumes usage [70]. SE Visible currently lacks white-label support according to its FAQ [71]. Anthropic-reported evidence describes SE Ranking as an SEO suite with added AI tracking rather than a category-native GEO tool [72]. .

9. AthenaHQ

Questions This Section Answers

  • Is AthenaHQ worth it for tracking recommendation share, and which features require an Enterprise plan?
  • How many credits does AthenaHQ's Self-Serve plan include, and what happens when they run out?
  • Is AthenaHQ or Profound better for recommendation share when multi-region tracking matters?

AthenaHQ ranked ninth, named by 2 of 7 platforms with an average listed position of 6.0 and a best position of 3. The AthenaHQ fit review covers the full evidence bundle.

Why it ranked here. AthenaHQ received a third-place ranking from anthropic and a ninth-place ranking from grok, producing a wide spread. Its eligibility came from meeting the two-platform minimum.

Best suited for. Marketing and SEO/GEO teams tracking recommendation rate, share of voice, mentions, citations, sentiment, and competitors across major AI answer platforms, and teams that want monitoring combined with content-gap analysis and prescriptive optimization recommendations.

Main strengths for the use case. AthenaHQ states that it tracks how AI platforms recommend, mention, and represent brands, including recommendation rate and share of voice [73]. Anthropic-reported evidence describes share of voice broken down by individual model, mention rate and position tracked over time, and citation rate rolled up [74]. The platform stores full AI answers and logs the prompt that triggered the mention, the full answer, and the position cited [75]. Competitive benchmarking shows which competitors are winning visibility on specific AI platforms [76].

Main limitations. Public materials do not clearly define the calculation methodology or prove that every result includes an explicit recommendation position [73]. The Athena Recommendation Engine and ACE Citation Engine are Enterprise-only, leaving Self-Serve users with basic tracking metrics [77]. Self-Serve is limited to single-country tracking [79]. API access requires an Enterprise contract per one source, while another describes it as a paid Starter add-on [80]. Credit-based pricing makes monthly spend unpredictable [82]. Long-term historical baselines are thinner than mature SEO tools because AI search surfaces are new [83].

Pricing or cost summary. The Self-Serve tier is reported at $295/month standard with a $95 first month, 3,600 monthly credits, and API access, subject to verification [73].

10. Rankscale

Questions This Section Answers

  • Is Rankscale worth it for tracking recommendation share, and how does its credit model affect cost?
  • Which AI engines does Rankscale track, and is API access included on the Pro plan?
  • What does Rankscale cost per month, and how many credits does each plan include?

Rankscale ranked tenth, named by 2 of 7 platforms with an average listed position of 7.0 and a best position of 7. The Rankscale fit review covers the full evidence bundle.

Why it ranked here. Rankscale received identical seventh-place rankings from both platforms that named it, producing the narrowest spread in the index. Its eligibility came from meeting the two-platform minimum.

Best suited for. Companies tracking whether their brand is recommended or listed for defined commercial and comparison prompts, SEO, GEO, and AEO teams needing recurring monitoring across ChatGPT, Gemini, Perplexity, Claude, Google AI Overviews, Copilot, and other engines, and agencies and multi-brand teams needing competitor comparisons, dashboards, exports, or API access on higher plans.

Main strengths for the use case. Rankscale states that it tracks whether brands are recommended, answer placement, prompt-level share of voice, competitors appearing above or beside the brand, and answer snapshots [84]. Anthropic-reported evidence describes position tracking with bands (#1–3 strong, #4–6 mid, #7+ weak, Not Found), citation tracking by domain and URL, share of voice, share of citations, and historical trend analysis [85]. The public pricing page lists ChatGPT, Gemini, Perplexity, Claude, DeepSeek, Mistral, Grok, Copilot, and additional engines [86]. Monitoring can be scheduled hourly, daily, weekly, or monthly [86].

Main limitations. Recommendation-share methodology and statistical normalization are not fully documented publicly [84]. Credit-based billing can make costs difficult to forecast when monitoring many prompts across many engines or at high frequency [86]. The $20 entry tier appears capacity-limited [86]. REST API access is associated with higher tiers rather than the entry-level plan [87]. Anthropic-reported evidence states that position tracking granularity is not disclosed, and no SLA or uptime guarantee is published [88]. Kimi stated that the meaning of "rank" in the product naming is ambiguous and that no public evidence confirms Rankscale parses whether a brand is mentioned, listed, or actively recommended [89]. .

What the Cross-Platform Study Reveals About This Market

Questions This Section Answers

  • What do the 7 AI platforms collectively reveal about which AI visibility tools actually track recommendation share?
  • Why do so many AI visibility platforms lack a clearly documented recommendation-position metric?

Three patterns emerge from the 7-platform study.

First, recommendation-level measurement is claimed far more often than it is documented. Profound, Peec AI, Semrush, OtterlyAI, Scrunch AI, Ahrefs Brand Radar, Writesonic, SE Ranking, AthenaHQ, and Rankscale all describe recommendation-oriented or position-oriented metrics, but multiple platforms independently noted that the exact calculation of recommendation share, recommendation position, or recommendation rate is not publicly specified [90].

Second, engine coverage is the most consistent differentiator. Profound's Growth plan covers three engines with Enterprise reaching up to 10 [100]. Peec AI's self-serve plans limit buyers to three selected models [91]. Semrush's base toolkit covers three to four platforms [102].

Where the AI Platforms Agreed

Questions This Section Answers

  • Which AI visibility platforms did all 7 AI platforms agree belong in the recommendation-share shortlist?
  • What capabilities did the AI platforms consistently attribute to the top-ranked AI visibility tools?

Profound was the only entity named by 6 of 7 platforms, and every naming platform placed it in the top three. Platforms agreed that Profound documents Share of Voice, Average Position, platform-level comparisons, competitor benchmarking, and historical trend analysis [103].

Peec AI and Semrush were both named by 5 of 7 platforms, and both received at least one top-two placement. Platforms agreed that Peec AI reports visibility, position, sentiment, and share of voice daily against competitors [107], and that Semrush reports AI Visibility Score, Share of Voice, Mentions, Cited Pages, and competitor comparisons [109].

Platforms also agreed on a shared limitation: none of the ten entities was described as having an independently audited or externally validated recommendation-share methodology. Every fit assessment that addressed methodology described it as vendor-reported or unclear.

Where the AI Platforms Disagreed

Questions This Section Answers

  • Why did some AI platforms rate the same AI visibility tool as strong while others rated it uncertain?
  • Which AI visibility platforms received the widest spread in fit ratings across the 7 AI platforms?

Fit ratings diverged most sharply for entities whose official sites were inaccessible during one platform's research. Kimi rated Profound, Peec AI, OtterlyAI, Scrunch AI, and AthenaHQ as uncertain, in several cases because official-site retrieval failed [111]. Other platforms rated the same entities good or strong.

Ahrefs Brand Radar received mixed ratings from six platforms and a good rating from one [116]. The disagreement centered on whether mention and citation tracking constitutes recommendation-share tracking.

Rankscale received strong ratings from grok and google and mixed or uncertain ratings from deepseek, perplexity, and kimi [117]. The disagreement centered on whether the product's "rank" terminology implies recommendation position or general visibility.

Pricing conflicts appeared across nearly every entity.

How Buyers Should Choose

Questions This Section Answers

  • What should a buyer check before choosing an AI visibility platform for tracking recommendation share?
  • Which AI visibility platform should a buyer choose if they need verified recommendation-position data rather than visibility proxies?

Start by defining what "recommendation share" means for your organization. If it means the percentage of tracked prompts where your brand appears in an AI answer, most platforms in this index can report it. If it means the ordinal position of your brand within a ranked recommendation list, only a subset of platforms clearly documents that capability, and several platforms independently flagged that this distinction is not publicly specified [122].

Second, count the engines you actually need. If Claude, Grok, Meta AI, or DeepSeek are mandatory, verify plan-level coverage in writing before purchase, because several platforms gate those engines behind Enterprise tiers [126].

Third, model the true cost.

Methodology

This index was produced from a single standardized prompt sent once to each of 7 AI platforms: openai, anthropic, deepseek, grok, perplexity, kimi, and google. The prompt asked which AI visibility platforms would be recommended for tracking AI recommendation share, and why, for a company wanting recommendation-level data rather than simple brand mentions, with platform-by-platform results, recommendation position, historical trends, and competitor comparisons.

The research date is 2026-09-19. Platform-reported research dates differ from this authoritative run date and are provenance metadata only; they do not independently prove freshness.

Entities qualified for the ranking table by being named by at least two platforms during ranking discovery. Platform mentions count only ranking-discovery mentions, not fit-assessment completions. The final ranking order is based on platform mentions, then average listed rank, then best listed rank.

Each qualifying entity was then assessed for fit against the use case, and those assessments form the entity evidence bundles used throughout this article. Citations are platform-reported evidence, not independently verified facts. Company-owned sources and independent sources are distinguished where the evidence bundles provide that information.

Methodology Limitations

Several limitations apply to every finding in this article.

The study used one standardized prompt sent once to each included platform. AI answers can vary by date, wording, location, account state, model, interface, browsing configuration, and the sources retrieved. A single run cannot capture that variance.

Platform recommendations are market intelligence, not independent customer reviews or proof of quality. No platform's recommendation should be read as an endorsement.

Platform-reported research dates differ from the authoritative run date of 2026-09-19. Deepseek reported 2026-06-01 for Profound, 2026-02-14 for Peec AI and OtterlyAI, 2026-06-11 for Semrush, 2026-06-12 for Ahrefs Brand Radar and Rankscale, 2026-01-15 for Writesonic, 2026-06-01 for SE Ranking, and 2026-04-26 for AthenaHQ. Anthropic reported 2026-01-17 for Rankscale. These dates are provenance metadata and do not independently prove freshness.

Official-site retrieval failed for one or more mentions of Profound, Peec AI, OtterlyAI, Scrunch AI, Ahrefs Brand Radar, and AthenaHQ. No failed fetch was used as a verified domain key. AthenaHQ's identity audit noted conflicting official domains and an unresolved identity resolved by exact-name fallback.

The supplied URLs were collected from platform responses and were not independently validated by the writer stage.

Final Verdict

Profound is the consensus leader for tracking AI recommendation share, named by 6 of 7 platforms with an average listed position of 1.67. Peec AI is the strongest alternative for teams that want prompt-level share-of-voice and citation reporting with unlimited seats at a lower entry price. Semrush and SE Ranking fit buyers who want AI recommendation tracking folded into an existing SEO workflow. OtterlyAI is the budget-conscious entry point. Scrunch AI adds an optimization layer. Ahrefs Brand Radar suits existing Ahrefs customers. Writesonic AI Visibility pairs monitoring with content remediation. AthenaHQ and Rankscale offer credit-based models with broad engine coverage.

No platform in this index was described as having an independently audited recommendation-share methodology. Buyers whose core KPI is verified recommendation position should require a demonstration against their own prompts before purchase.

Frequently Asked Questions

What is the best AI visibility platform for tracking recommendation share in 2026?

Profound ranked first, named by 6 of 7 platforms with an average listed position of 1.67. Peec AI ranked second, named by 5 of 7 platforms with a best position of 1.

How many platforms were studied for this index?

Seven: openai, anthropic, deepseek, grok, perplexity, kimi, and google. One standardized prompt was sent once to each.

How many entities qualified for the ranking?

Ten entities qualified out of 34 unique entities named, using the rule that an entity must be named by at least two platforms during ranking discovery.

Do any of these platforms report recommendation position separately from brand mentions?

Several platforms document position-oriented metrics, but multiple platforms independently noted that the exact calculation of recommendation share or recommendation position is not publicly specified. Buyers should verify this capability in a demonstration.

Why did some platforms rate the same tool differently?

Fit ratings diverged most for entities whose official sites were inaccessible during one platform's research, and for entities where the distinction between mention tracking and recommendation tracking was ambiguous. .

From the index

Compare the adjacent findings before forming a view.

Consolidated Sources

Company-Owned Sources

Independent Sources

Other Sources

Platform-by-platform recommendations

Numbers show recorded recommendation position. A dash means no qualifying recommendation was recorded in a usable response. Unusable responses are not negative votes.

Qualified entities in this research snapshot
PlatformProfoundPeec AISemrushOtterlyAIScrunch AIAhrefs Brand RadarWritesonic AI VisibilitySE RankingAthenaHQRankscale
ChatGPT#2#1#6#5#3#4————
Claude#1#2—#6#4—#8#5#3#7
DeepSeek#2#1#3#7#8#4————
Grok#1#5#7#6#8———#9—
Perplexity#1#6#2#4—#5#9#3—#7
Kimi——————#7———
Gemini#3—#6———————

Verify this research

Review the study details behind this page or download the public machine-readable verification record.

Study date
September 19, 2026
Platforms analyzed
7
Candidates reviewed
34
Qualified finalists
10

Research trail and source mix

Configured platforms

openai, anthropic, deepseek, grok, perplexity, kimi, google

Source mix

369 total · 213 independent · 154 company-owned · 2 unclear

Evidence support

273 direct · 66 partial

Important limitation

Exactly 7 platforms were included in this run: openai, anthropic, deepseek, grok, perplexity, kimi, google. The configured source value 7 is provenance only and must never be described as the number of platforms studied.

Source snapshot SHA-256 fdf14d041f872d2311086068b2d4c7a1b2af7bd372431d7a2fab4efd4504f55e