Best AI Search Intelligence Platforms for Recommendation Share

Answer Capsule

Profound is the consensus leader for AI Search Intelligence Platforms for Recommendation Share, named by 6 of 7 platforms (85.7% share) at an average listed position of 2.0 and a best position of 1. Otterly (5 mentions, 71.4%) and Semrush (5 mentions, 71.4%) are the strongest alternatives for buyers who need lower entry pricing or unified SEO-plus-AI workflows. AthenaHQ and Peec AI lead on explicit recommendation-rate and product-level recommendation tracking, while Ahrefs, Goodie, Conductor, and Similarweb serve narrower enterprise or commerce-specific needs. This study covered 7 platforms (openai, anthropic, deepseek, grok, perplexity, kimi, google), named 31 unique entities, and qualified 9 under a two-platform minimum. The principal limitation: the study used one standardized prompt sent once to each platform, and no platform publicly documents a fully audited, denominator-transparent recommendation-share metric distinct from mention share.

Research Snapshot

• Topic: Best AI Search Intelligence Platforms for Recommendation Share
• Target buyer: Companies seeking AI Search Intelligence Platforms for Recommendation Share across AI search, generative-answer, and recommendation platforms
• Geography: United States
• Platforms included (7): openai, anthropic, deepseek, grok, perplexity, kimi, google
• Research date: 2026-09-18
• Unique entities named: 31
• Qualifying entities: 9
• Eligibility rule: Named by at least two platforms during ranking discovery
• Ranking unit: Software platform or research platform
• Identity scope: Company

Platform mentions count only ranking-discovery mentions. Several platforms that did not name an entity during discovery still completed a fit assessment for it; those assessments inform the entity sections but do not change the ranking table.

The Consensus Ranking

Questions This Section Answers

• What are the best AI search intelligence platforms for recommendation share in 2026?
• Which platform did the most AI platforms name first for measuring recommendation share?
• How many platforms named Profound, Otterly, and Semrush for AI recommendation-share tracking?

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

Questions This Section Answers

• Which AI search intelligence platform should a buyer choose for recommendation share if they need the lowest published entry price?
• Is Profound or Otterly better for recommendation share when multi-engine coverage and enterprise governance matter?
• Which platform should an e-commerce brand choose for SKU-level AI recommendation tracking?

1. Profound

Questions This Section Answers

• Is Profound worth it for AI recommendation-share tracking, and what are its main drawbacks?
• Which Profound plan covers the most AI answer engines for recommendation monitoring?
• Does Profound separate recommendation share from simple mention share?

Profound is the consensus leader for this use case, named by 6 of 7 platforms at an average listed position of 2.0 and a best position of 1. It is the only entity in this study that combined top-two placement on four platforms with a documented metric set covering visibility, share of voice, average position, citation rank, and competitor comparison ^[1]. The Profound fit review covers the full evidence bundle.

Why it ranked here. Profound appeared on openai (rank 3), anthropic (rank 2), deepseek (rank 1), google (rank 1), grok (rank 1), and perplexity (rank 4). No other entity received a first-place listing from more than one platform. Its platform share of 85.7% is the highest in the study.

Best suited for. Enterprise marketing, SEO, PR, and brand teams tracking AI-answer visibility across multiple engines, competitors, regions, topics, and time periods; buyers needing daily prompt tracking, average position, share-of-voice reporting, citation analysis, sentiment, dashboards, and workflow activation; organizations willing to treat recommendation share as a customized analysis rather than an automatically validated native metric.

Main strengths for this use case. Profound documents Share of Voice as the percentage of brand mentions relative to total brand mentions and Average Position as the relative order in which a brand is mentioned, with a score of 1 meaning the brand is typically mentioned first ^[1]. Answer Engine Insights supports platform-level breakdowns, and the public pricing page lists ChatGPT, Perplexity, and Google AI Overviews for Growth, with broader capability and up to nine answer engines under Enterprise ^[3]. Daily prompt execution, trend lines, date-range filters, topic and prompt rankings, and platform, region, persona, and topic filters support historical and category comparisons ^[4]. Google's bundle reports that Profound records median position and specific placement metrics, distinguishing sequential list recommendations from simple inline mentions, and that a dedicated Shopping Analysis module isolates SKU-level product recommendations inside carousel tiles ^[5]. Anthropic's bundle adds that Profound captures real-user data directly from consumer browsing experience rather than model APIs ^[6].

Main limitations. Profound's documentation states that Visibility Score and Mention Frequency do not currently expose a native total-opportunity denominator covering all responses in a selected prompt set when no brand is mentioned, so buyers needing recommendation share over all eligible recommendation opportunities may need manual or assisted calculation ^[7].

2. Otterly

Questions This Section Answers

• Is Otterly worth it for AI recommendation-share monitoring, and what are its main drawbacks?
• Which Otterly plan should a buyer choose if they need Gemini, Google AI Mode, and Claude coverage?
• How much does Otterly cost per month for 100 tracked prompts?

Otterly ranked second with 5 platform mentions (71.4% share), an average listed position of 4.4, and a best position of 2. It is the lowest-cost structured entry point in this study and the only entity whose official pricing page was retrieved with plan-level detail [e2:official:C2]. The Otterly fit review covers the full evidence bundle.

Why it ranked here. Otterly appeared on openai (rank 5), anthropic (rank 4), grok (rank 3), perplexity (rank 2), and google (rank 8). Its best position of 2 came from Perplexity. Google's lower placement reflects add-on pricing and missing engine coverage.

Best suited for. Teams needing recurring monitoring of brand and competitor visibility across ChatGPT, Google AI Overviews, Perplexity, and Microsoft Copilot; buyers needing prompt-level competitor ranking, answer-position analysis, citation tracking, and trend reporting; marketing teams that want GEO recommendations in addition to measurement.

Main strengths for this use case. Otterly's prompt detail analysis ranks brands by frequency and prominence and supports tracking competitor position over time ^[8]. The platform treats Google AI Overviews and Google AI Mode as separate engines because their behavior and citations differ ^[9]. It tracks cited URLs, domain or URL citation frequency, link-position changes, and citation winners and losers ^[10]. Daily prompt tracking, trend lines, brand coverage over time, and competitor-position tracking support historical analysis ^[11]. Standard and higher plans list API and MCP access ^[12]. Independent reviews report a G2 average of 4.7 out of 5 across 54 ratings and recognition as a Gartner 2025 Cool Vendor ^[13].

Main limitations. Public documentation emphasizes mentions, coverage, share of voice, citations, and ranking; it does not clearly define recommendation share as a distinct normalized metric ^[14]. Share of voice is described as the share of brand mentions across tracked brands, which may not measure the proportion of answers that actively recommend a brand ^[15]. Core plans cover four engines; Gemini, Claude, and Google AI Mode incur add-on charges ^[16]. The cancellation help page states that tracked engines and historical data will be deleted after cancellation ^[17]. Google's bundle reports that Otterly does not support Grok, Meta AI, or DeepSeek at any pricing tier ^[18]. Kimi found no independent verification of Otterly's core capabilities ^[19]. .

3. Semrush

Questions This Section Answers

• Is Semrush worth it for AI recommendation-share tracking, and what are its main drawbacks?
• Which Semrush plan should a buyer choose if they need Claude and Microsoft Copilot coverage?
• How much does the Semrush AI Visibility Toolkit cost per domain per month?

Semrush ranked third with 5 platform mentions (71.4% share), an average listed position of 5.2, and a best position of 2. It is the strongest option for buyers who want AI visibility and traditional SEO intelligence under one contract. The Semrush fit review covers the full evidence bundle.

Why it ranked here. Semrush appeared on openai (rank 2), anthropic (rank 5), deepseek (rank 6), perplexity (rank 6), and google (rank 7). Its best position of 2 came from OpenAI. Google and Grok both rated it a strong fit; OpenAI, DeepSeek, Perplexity, and Kimi rated it mixed.

Best suited for. Companies wanting AI visibility and SEO intelligence in one platform; enterprise teams tracking multiple brands, products, markets, regions, and AI platforms; buyers needing competitor comparisons, prompt research, historical trends, reporting, and optimization workflows.

Main strengths for this use case. Semrush tracks both visibility and share-of-voice metrics as core KPIs, with visibility representing presence and share-of-voice measuring relative competitive position ^[20]. Brand Performance reports track share of voice, sentiment, and AI-generated narratives weekly ^[21]. Competitor Research auto-identifies brands you are measured against ^[22]. Google's bundle reports that the Brand Performance report calculates AI Share of Voice based on both the number of times a brand is mentioned and how highly it is positioned in LLM responses ^[23]. Semrush also distinguishes an AI mention, an AI citation, and an AI source ^[23]. Enterprise AIO provides access to 289M+ relevant LLM prompts globally ^[24].

Main limitations. Standard dashboards do not separate recommendation-level positioning; users must inspect answer snapshots manually, limiting scalability for large prompt sets ^[25]. Claude is missing from Semrush One and available only in Enterprise AIO ^[26]. The AI Visibility Toolkit provides data for US English only ^[27]. The standalone tier tracks 25 custom prompts per domain, which reviewers note produces a wide error bar ^[28]. Independent reviewers note that tracking how AI engines mention and recommend brands is not Semrush's core strength ^[29]. DeepSeek's bundle reports that whether Semrush isolates a distinct recommendation-share metric separate from mention share is unclear from public materials ^[30]. .

4. AthenaHQ

Questions This Section Answers

• Is AthenaHQ worth it for AI recommendation-share tracking, and what are its main drawbacks?
• How many credits does the AthenaHQ Starter plan include, and what happens when they run out?
• Which AthenaHQ features are locked to the Enterprise plan?

AthenaHQ ranked fourth with 4 platform mentions (57.1% share), an average listed position of 3.75, and a best position of 1. It received the highest average position of any entity outside the top three and the only first-place listing from Anthropic. The AthenaHQ fit review covers the full evidence bundle.

Why it ranked here. AthenaHQ appeared on anthropic (rank 1), deepseek (rank 3), google (rank 4), and perplexity (rank 7). Its best position of 1 came from Anthropic. Google rated it good and highlighted explicit recommendation-frequency and position tracking; Perplexity rated it mixed because public evidence does not clearly verify a dedicated recommendation-share metric.

Best suited for. Marketing and SEO teams monitoring brand recommendations and visibility across multiple AI-search platforms; companies needing competitor benchmarking, citation-source analysis, content-gap analysis, and optimization actions in one platform; teams willing to validate metric definitions, sampling methodology, prompt volume, and historical-data availability before purchase.

Main strengths for this use case. AthenaHQ measures share of voice as the frequency an AI model actively selects a brand for inclusion in a synthesized answer, distinct from mere mentions ^[31]. The platform logs the prompt that triggered the mention, the full answer, and citation position, then stitches snapshots into share-of-voice trend lines sliced by engine, region, and topic ^[32]. Starter provides visibility across 11 AI models including ChatGPT, Perplexity, Google AI Overviews, Google AI Mode, Gemini, Claude, Copilot, Grok, DeepSeek, Meta AI, and Mistral ^[33]. Google's bundle reports that AthenaHQ tracks explicit recommendation frequency, position, and category share of voice in real time rather than just capturing simple brand mentions ^[34]. The platform integrates with Shopify and Google Analytics 4 to tie AI search recommendation visibility to site sessions and revenue attribution ^[35].

Main limitations. Credit-based pricing makes monthly costs unpredictable; high monitoring intensity, prompt volume increases, or heavy Ask Athena usage consume credits faster than expected ^[36]. Self-Serve is limited to a single country, forcing multi-region organizations into Enterprise ^[37]. The Athena Recommendation Engine and Athena Citation Engine are gated behind the custom-priced Enterprise tier ^[38]. AthenaHQ emerged from stealth in early 2025, so limited deep historical baselines exist for long-term trend modeling ^[39]. Public independent evidence for hallucination-detection accuracy and revenue attribution remains limited ^[40].

5. Peec AI

Questions This Section Answers

• Is Peec AI worth it for AI recommendation-share tracking, and what are its main drawbacks?
• How much does Peec AI charge per additional AI model beyond the three included in self-serve plans?
• Which Peec AI plan should an e-commerce brand choose for SKU-level recommendation tracking?

Peec AI ranked fifth with 3 platform mentions (42.9% share), an average listed position of 4.0, and a best position of 1. It received OpenAI's first-place listing and has the strongest documented product-level recommendation evidence in the study. The Peec AI fit review covers the full evidence bundle.

Why it ranked here. Peec AI appeared on openai (rank 1), deepseek (rank 2), and perplexity (rank 9). Its best position of 1 came from OpenAI. Google and Grok rated it good; DeepSeek, Kimi, and Perplexity rated it uncertain or mixed because public evidence does not clearly verify a recommendation-share metric distinct from mention share.

Best suited for. Brands and marketing teams measuring recommendation visibility and competitive position across tracked AI search models; e-commerce companies needing SKU-level visibility, product position, win rate, shopping-query analysis, and destination-source reporting; agencies requiring multiple projects, client reporting, and centralized prompt allocation.

Main strengths for this use case. For AI shopping, Peec defines visibility as the share of AI shopping answers in which a product appears and provides brand-level share of voice over time ^[41]. Win rate is described as the share of answers in which a product appears first ^[41]. Google's bundle reports that AI Shopping Analytics tracks specific SKUs inside conversational commerce surfaces like ChatGPT's product carousel, identifying whether a product is actively recommended, its win rate, and its relative position ^[42]. Peec uses UI scraping technology to simulate real browser interactions rather than API-based monitoring, capturing the same responses actual users see ^[43]. It distinguishes brand visibility from source visibility and tracks both named mentions and URL citations separately ^[44]. The platform supports multi-language and multi-region monitoring at no additional per-country surcharge ^[43].

Main limitations. Base plans include only three AI models; tracking four or more requires paid add-ons, with only six models available self-serve and newer models reserved for Enterprise ^[45]. Public documentation does not fully disclose the methodology for classifying direct recommendations versus indirect or incidental mentions ^[46]. Peec's public materials state that AI models only see HTML content and cannot read content behind paywalls or load JavaScript-dependent content ^[46]. The platform does not include content optimization tools, site audits, or AI traffic attribution ^[47].

6. Ahrefs

Questions This Section Answers

• Is Ahrefs Brand Radar worth it for AI recommendation-share tracking, and what are its main drawbacks?
• How much does full Ahrefs Brand Radar coverage cost per month including the required base plan?
• Does Ahrefs Brand Radar track Claude and Grok for AI recommendation monitoring?

Ahrefs ranked sixth with 3 platform mentions (42.9% share), an average listed position of 7.0, and a best position of 4. It is the only entity in the study whose average listed position fell outside the top five, reflecting consistent concerns about recommendation-specific depth. The Ahrefs fit review covers the full evidence bundle.

Why it ranked here. Ahrefs appeared on openai (rank 4), deepseek (rank 7), and perplexity (rank 10). Its best position of 4 came from OpenAI. Google rated it good; OpenAI, Anthropic, DeepSeek, Grok, and Perplexity rated it mixed; Kimi rated it weak.

Best suited for. Companies needing broad AI-search visibility and competitor benchmarking across major AI answer platforms; teams monitoring exact buyer questions with custom prompts, locations, platforms, and refresh schedules; SEO and AEO teams that want AI visibility connected to search demand, cited pages, domains, and content opportunities.

Main strengths for this use case. Brand Radar's AI Visibility Index covers Google AI Overviews, Google AI Mode, ChatGPT, Perplexity, Gemini, and Microsoft Copilot; Custom Prompts also support Claude, with platform and location selection ^[48]. The AI Visibility Index provides historical responses back to 2025 ^[48]. Ahrefs states that AI-platform prompts are collected at locations matching the underlying keyword data and that supported web-platform responses are captured without stored user data, prior context, normalization, personalization, pre-prompting, or filtering ^[49]. Google's bundle reports that Brand Radar uses robust filters to segment results by whether a brand is simply mentioned inline or directly cited via a source link ^[50]. The platform integrates AI visibility with Ahrefs' backlink, keyword, YouTube, Reddit, and search-demand data ^[51].

Main limitations. Public documentation centers on visibility, mentions, citations, impressions, and AI Share of Voice rather than a clearly defined recommendation-share metric ^[52]. Recommendation position and positive recommendation intent are not clearly documented as standardized outputs ^[53]. Custom Prompt checks are consumed per prompt execution, platform, and location, so broad multi-platform monitoring can become expensive ^[54]. Independent testing reported a 97.5% undercount in one ChatGPT mention test ^[55]. Anthropic's bundle reports no Claude or Grok tracking in the reviewed configuration, calling it a major blind spot ^[56]. Grok's bundle reports that AI prompt tracking frequency is limited to weekly ^[57]. .

7. Goodie

Questions This Section Answers

• Is Goodie worth it for AI recommendation-share tracking, and what are its main drawbacks?
• Which Goodie plan should a buyer choose if they need Claude, Gemini, or Amazon Rufus coverage?
• How much does the Goodie Core plan cost per month, and how many prompts does it include?

Goodie ranked seventh with 2 platform mentions (28.6% share), an average listed position of 4.5, and a best position of 3. It received Anthropic's third-place listing and Google's strong-fit rating for agentic-commerce recommendation tracking. The Goodie fit review covers the full evidence bundle.

Why it ranked here. Goodie appeared on anthropic (rank 3) and google (rank 6). Its best position of 3 came from Anthropic. Google rated it strong; OpenAI, Anthropic, and Grok rated it good; Perplexity rated it mixed; DeepSeek and Kimi rated it uncertain.

Best suited for. Mid-market and enterprise teams monitoring brand recommendations across ChatGPT, Google AI surfaces, Perplexity, Gemini, Copilot, Claude, and related platforms; teams that want monitoring combined with optimization actions, prompt research, competitive benchmarking, and attribution; organizations needing model-, geography-, persona-, language-, and topic-level segmentation.

Main strengths for this use case. Goodie states that its visibility monitoring measures how often a brand is recommended versus competitors and reports mention frequency, competitive share of voice, and recommendation-related visibility ^[58]. It publicly distinguishes a brand being named as the recommendation from a brand merely appearing as a citation or mention ^[59]. Google's bundle reports that Goodie tracks recommendation positioning inside synthesized AI comparison sets and that its Agentic Commerce Suite tracks recommendation frequency for e-commerce brands on ChatGPT Shopping, Amazon Rufus, and Perplexity Shopping ^[60]. The platform monitors 11+ AI engines including ChatGPT, Perplexity, Gemini, Google AI Overviews, Claude, Microsoft Copilot, Google AI Mode, Amazon Rufus, Meta AI, DeepSeek, and Grok ^[61]. Goodie's Optimization Hub provides prioritized recommendations scored by citation weight and estimated impact ^[62].

Main limitations. Public documentation does not fully specify the calculation of recommendation share or its separation from mention, citation, and share-of-voice metrics ^[63]. Most supporting evidence is company-owned; independent validation of measurement quality was not located ^[64]. Prompt capacity is finite on published plans ^[63]. Anthropic's bundle reports that Goodie's share-of-voice metric is built on mentions and does not isolate recommendation-position tracking as a distinct metric ^[65]. The requested AI Explorer and Standard plan labels conflict with the current public pricing labels of Core, Pro, and Enterprise ^[63].

8. Conductor

Questions This Section Answers

• Is Conductor worth it for AI recommendation-share tracking, and what are its main drawbacks?
• How much do Conductor enterprise contracts typically cost per year, and does the entry tier include AI search credits?
• Does Conductor distinguish recommendation share from mention share in its AI Search Performance module?

Conductor ranked eighth with 2 platform mentions (28.6% share), an average listed position of 4.5, and a best position of 4. It is the strongest option in this study for enterprises that need mention-versus-citation separation inside a broader SEO and content platform. The Conductor fit review covers the full evidence bundle.

Why it ranked here. Conductor appeared on kimi (rank 4) and perplexity (rank 5). Its best position of 4 came from Kimi. Anthropic, Google, and DeepSeek rated it good or mixed; Kimi rated it weak; Grok and Perplexity rated it mixed.

Best suited for. Enterprise teams wanting AI visibility measurement integrated with SEO, content, website, and business-performance data; companies comparing AI visibility across ChatGPT, Perplexity, Google AI Overviews/AI Mode, and other supported engines; organizations needing competitor benchmarking, topic and prompt analysis, historical monitoring, APIs, or enterprise-scale governance.

Main strengths for this use case. Conductor calculates two distinct metrics: mention-based share of voice (brand presence in conversation) and citation-based share of voice (authoritative sources driving AI traffic) ^[66]. It measures visibility in two distinct ways: Brand Mentions and Website Citations ^[67]. The platform states that AI engines differ and supports visibility analysis across ChatGPT, Perplexity, Google AI Overviews, Google AI Mode, Copilot, Gemini, and other engines, with separate tracking for ChatGPT Auto and Search modes ^[68]. Conductor analyzed 13,770 domains against an index of 3.5 million unique prompts between May and September 2025 with 17 million AI-generated responses ^[69]. Google's bundle reports that Conductor partnered with Noble to help brands manage offsite citations in the articles, blogs, and listicles that LLMs crawl ^[70].

Main limitations. Public descriptions distinguish visibility, mentions, citations, sentiment, and share of voice, but do not clearly define an independent recommendation-share calculation ^[71]. The reviewed materials do not clearly specify recommendation-frequency fields, recommendation position/rank, or a separate recommendation-share metric ^[72]. Pricing and contractual terms are not sufficiently public for a reliable total-cost comparison ^[73]. Grok's bundle reports that AI prompt tracking frequency is limited to weekly ^[74]. Kimi rated Conductor weak, arguing its core platform is built for SEO intelligence rather than recommendation system measurement ^[75].

9. Similarweb

Questions This Section Answers

• Is Similarweb worth it for AI recommendation-share tracking, and what are its main drawbacks?
• How much does Similarweb AI Search Intelligence cost per month, and how many prompts does the entry plan include?
• Does Similarweb track the same AI engines for brand visibility and referral traffic?

Similarweb ranked ninth with 2 platform mentions (28.6% share), an average listed position of 5.5, and a best position of 5. It is the only entity in the study whose best position was 5, reflecting consistent concerns about recommendation-specific depth. The Similarweb fit review covers the full evidence bundle.

Why it ranked here. Similarweb appeared on deepseek (rank 5) and kimi (rank 6). Its best position of 5 came from DeepSeek. OpenAI, Anthropic, DeepSeek, and Google rated it good; Grok and Perplexity rated it mixed; Kimi rated it uncertain.

Best suited for. Companies needing AI visibility and competitive share-of-voice monitoring across ChatGPT, Gemini, Perplexity, and Google AI Mode; teams that want prompt-level answers, citation analysis, sentiment, historical trends, and AI referral-traffic measurement in one platform; marketers that value Similarweb's broader SEO, competitive-intelligence, and web-traffic datasets.

Main strengths for this use case. Similarweb reports AI Brand Visibility and mention share across configured topics and competitors ^[76]. The standalone AEO Intelligence plan publicly lists three months of historical data; the $333 tier lists six months ^[77]. Similarweb states that its AI Search Intelligence can provide competitive AI share of voice across up to ten competitors ^[78]. Google's bundle reports that Similarweb distinguishes between AI Brand Mention Share and AI Citation Share, preventing teams from conflating simple mentions with validated recommendation authority ^[79]. The platform is built on normalized real-user query data rather than synthetic prompt simulations ^[80]. Similarweb connects AI visibility directly to downstream brand visibility and traffic through its existing web-analytics infrastructure ^[81].

Main limitations. Public documentation emphasizes visibility and mention share more clearly than a separately defined recommendation-share metric ^[76]. Recommendation position and position-weighted share are not clearly documented ^[77]. The published entry plans have one user and 150 tracked prompts ^[77]. Company materials acknowledge that a mention does not automatically represent a recommendation and a citation does not necessarily generate traffic ^[82]. Google's bundle reports that engine tracking is asymmetrical: brand visibility covers four platforms while referral traffic covers a different set ^[83]. Kimi found no independent verification of core recommendation-share tracking capabilities ^[84]. .

What the Cross-Platform Study Reveals About This Market

Questions This Section Answers

• What does the cross-platform study reveal about how AI platforms evaluate recommendation-share tools?
• Which recommendation-share capabilities do most AI search intelligence platforms still fail to document publicly?

Three patterns dominate this market. First, no qualifying platform publicly documents a fully audited recommendation-share metric with a transparent denominator that separates explicit recommendations, ranked recommendations, neutral mentions, and omissions. Profound explicitly states that its Visibility Score and Mention Frequency do not expose a native total-opportunity denominator ^[85]. Otterly's share of voice is described as the share of brand mentions across tracked brands ^[86]. Semrush requires manual answer-snapshot inspection to separate recommendation-level positioning ^[87]. Ahrefs documents Mentions, Citations, Impressions, and AI Share of Voice but not a recommendation-share calculation ^[88]. Similarweb acknowledges that a mention does not automatically represent a recommendation ^[89].

Second, the platforms that come closest to explicit recommendation tracking are the ones with the fewest ranking-discovery mentions. AthenaHQ publicly claims recommendation rate and share of voice and tracks position within answers ^[90], but was named by only 4 platforms. Peec AI tracks product visibility, first-place win rate, and position for AI shopping ^[91], but was named by only 3.

Where the AI Platforms Agreed

Questions This Section Answers

• Which AI search intelligence platforms did most AI platforms agree are good fits for recommendation share?
• What limitations did AI platforms consistently identify across Profound, Otterly, and Semrush?

Agreement clustered around four points.

Profound belongs at the top. Six of seven platforms named it, and four placed it in the top two. OpenAI, Anthropic, DeepSeek, Perplexity, and Google all rated it good or strong. Even Kimi, which rated it weak, acknowledged it operates in the adjacent AI visibility space ^[92].

Recommendation share is not a native metric anywhere. OpenAI, Anthropic, DeepSeek, Perplexity, Grok, and Kimi all independently flagged that Profound does not clearly separate recommendation share from mention share ^[93]. The same concern appears for Otterly, Semrush, Ahrefs, Goodie, Conductor, and Similarweb.

Pricing opacity is widespread. Profound's enterprise terms are undisclosed ^[98]. Semrush's Enterprise AIO pricing is custom ^[99]. Ahrefs requires a base plan plus add-ons ^[100]. Goodie's Pro and Enterprise tiers require a demo ^[101]. Conductor publishes no dollar schedule ^[102]. Only Otterly, Peec AI, AthenaHQ, and Similarweb publish entry-tier prices. .

Where the AI Platforms Disagreed

Questions This Section Answers

• Why did AI platforms disagree about whether Profound, Semrush, and Ahrefs fit recommendation-share tracking?
• Which platforms rated the same AI search intelligence tool as both strong and weak?

Disagreement was sharpest on four entities.

Profound: strong versus weak. Google rated Profound a strong fit and highlighted SKU-level Shopping Analysis that isolates product recommendations inside carousel tiles ^[103]. Kimi rated it weak, arguing its Generative Engine Optimization platform fundamentally addresses how brands appear in generative AI text answers, not how they are recommended by algorithmic recommendation systems ^[104]. Grok rated it mixed because of the unresolved domain identity conflict between profound.com and tryprofound.com ^[105].

Semrush: strong versus mixed. Grok rated Semrush strong, citing integrated share-of-voice, position, and sentiment tracking across five AI platforms ^[106]. Google rated it strong and credited it with distinguishing mentions, citations, and sources ^[107]. Kimi rated it mixed, arguing the platform is primarily SEO intelligence rather than recommendation-share tracking ^[108]. OpenAI rated it mixed because recommendation share is not clearly documented as a distinct metric ^[109].

Ahrefs: good versus weak. Google rated Ahrefs good and highlighted its search-backed prompt dataset and mention-versus-citation filters ^[110].

How Buyers Should Choose

Questions This Section Answers

• What should a buyer check before choosing an AI search intelligence platform for recommendation share?
• Which AI search intelligence platform should a buyer choose if they need a written recommendation-share metric definition before purchase?

Start with the metric definition, not the vendor. Ask every shortlisted vendor to provide a written definition of recommendation share, including the denominator, how explicit recommendations are classified separately from neutral mentions, and how ties, multiple recommendations, list position, product variants, and ambiguous brand mentions are handled. Profound's own verification list asks whether the platform can classify each result as explicit recommendation, ranked recommendation, neutral mention, negative mention, or omission ^[111]. AthenaHQ buyers should ask how the platform defines and measures recommendation versus mention and whether that definition is configurable ^[112]. Peec AI buyers should ask whether the platform tracks recommendation rate as a distinct metric from visibility and whether it is available on Pro and Advanced plans or only Enterprise ^[113].

Second, confirm engine coverage against the exact quoted plan. Profound's Growth tier covers three engines while Enterprise covers up to nine ^[114]. Otterly's core plans cover four engines with Gemini, Google AI Mode, and Claude as paid add-ons ^[115].

Methodology

This index used one standardized prompt sent once to each of 7 included platforms: openai, anthropic, deepseek, grok, perplexity, kimi, and google. The prompt asked which AI search intelligence platforms would be recommended for calculating how much of the AI recommendation landscape a company controls relative to competitors across a defined universe of commercially important prompts, with recommendation frequency, recommendation position, platform-level differences, historical trends, category comparisons, and the ability to distinguish recommendation share from simple mention share.

The ranking order is based on platform mentions, then average listed rank, then best listed rank. Platform mentions count only ranking-discovery mentions. All included platforms evaluated fit, but platform_mentions counts only platforms that named the entity during ranking discovery. The final ranking table is the sole authority for rank, platform mentions, platform share, average rank, and best rank.

Entities qualified if they were named by at least two platforms. Of 31 unique entities named, 9 qualified. The study date is 2026-09-18. Platform-reported research dates differ from the authoritative run date and are provenance metadata only; they do not independently prove freshness.

Citations are platform-reported evidence, not independently verified facts. The URLs were collected from platform responses and were not independently validated by the writer stage.

Methodology Limitations

• 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.
• Platform recommendations are market intelligence, not independent customer reviews or proof of quality.
• Platform-reported research dates differ from the authoritative run date. Anthropic reported 2026-01-15 and DeepSeek reported 2026-01-28 for Profound; DeepSeek reported 2026-01-15 for Otterly; DeepSeek reported 2026-01-13 for Semrush; DeepSeek reported 2026-02-14 for AthenaHQ; DeepSeek reported 2026-01-15 for Peec AI; DeepSeek reported 2026-06-01 for Ahrefs; DeepSeek reported 2026-06-11 for Goodie and Conductor; DeepSeek reported 2025-11-11 for Similarweb. These are provenance metadata and do not independently prove freshness.
• Conflicting official domains forced an unresolved identity for Profound. The identity audit used exact-name fallback, and the matching reported domain was retained for downstream research but remains unverified.
• Official-site retrieval failed for one or more mentions for Profound, Peec AI, Ahrefs, and Similarweb. No failed fetch was used as a verified domain key.
• Company-owned citations materially outnumber independent citations for Profound, Semrush, AthenaHQ, Ahrefs, Goodie, Conductor, and Similarweb. Do not describe company claims as independently verified.
• The deterministic identity audit contains qualification notes that must be disclosed where relevant. Company-name variants were collapsed onto one canonical brand before minimum_mentions qualification for Ahrefs.
• Pricing, plan names, and feature availability change frequently.

Final Verdict

Profound is the consensus leader for AI Search Intelligence Platforms for Recommendation Share, named by 6 of 7 platforms at an average listed position of 2.0. It offers the broadest documented combination of daily prompt tracking, share of voice, average position, citation analysis, platform-level breakdowns, and historical trends. Its principal weakness is that its documented core metrics are visibility and brand-mention metrics, and it does not expose a native all-response opportunity denominator.

Otterly and Semrush are the strongest alternatives for distinct buyer needs. Otterly wins on entry price and citation diagnostics. Semrush wins on unified SEO-plus-AI workflows and enterprise segmentation. AthenaHQ and Peec AI lead on explicit recommendation-rate and product-level recommendation tracking but carry credit-based pricing and plan gating. Ahrefs, Goodie, Conductor, and Similarweb serve narrower enterprise, commerce, governance, or traffic-attribution needs.

No platform in this study publicly documents a fully audited recommendation-share metric with a transparent denominator. Buyers whose primary KPI is a defensible recommendation-share number should require a written metric definition, a sample export showing recommendation share separately from mention share, and raw-response access before committing.