Best AI Recommendation Intelligence Platforms

Answer Capsule

Peec AI is the consensus leader in this 7-platform study of AI recommendation intelligence platforms, named by 4 of 7 platforms (57.1%) with an average listed position of 2.5 and a best position of 1. OtterlyAI ties on mentions (4 of 7, 57.1%) but ranks lower on average position (4.0). Profound (tryprofound.com) earned the strongest average position (1.5) but appeared on only 2 platforms. The strongest alternatives for distinct buyer needs are Profound for enterprise-scale prompt intelligence, OtterlyAI for low-cost multi-engine monitoring, and Loamly for forensic recommendation-versus-mention diagnosis. Seven platforms were studied: OpenAI, Anthropic, DeepSeek, Grok, Perplexity, Kimi, and Google. The principal limitation is that this study used one standardized prompt sent once to each platform, and platform answers vary by date, wording, location, account state, model, interface, browsing configuration, and retrieved sources. Platform recommendations are market intelligence, not independent customer reviews or proof of quality.

Research Snapshot

• Topic: Best AI search audits and market intelligence for AI Recommendation Intelligence Platforms
• Target buyer: Companies seeking AI Recommendation Intelligence Platforms across AI search, generative-answer, and recommendation platforms
• Platforms included: openai, anthropic, deepseek, grok, perplexity, kimi, google (7 platforms)
• Research date: 2026-09-18
• Unique entities named: 49
• Qualifying entities: 7
• Eligibility rule: Named by at least two platforms during ranking discovery
• Ranking unit: Software platform or research platform
• Geography: United States

The study used one standardized prompt sent once to each included platform. Platform-reported research dates differ from the authoritative run date: DeepSeek's response carried a 2026-06-15 date for Peec AI, 2026-01-15 for OtterlyAI, 2026-02-14 for Profound and Scrunch AI, and 2026-02-14 for Ahrefs, Loamly, and HubSpot AEO. These are provenance metadata and do not independently prove freshness.

The Consensus Ranking

Questions This Section Answers

• What are the best AI recommendation intelligence platforms for tracking which brands AI systems recommend in 2026?
• Which AI recommendation intelligence platform has the most platform mentions across OpenAI, Anthropic, DeepSeek, Grok, Perplexity, Kimi, and Google?
• Which AI recommendation intelligence platform ranks highest on average listed position even with fewer platform mentions?

The ranking below uses the supplied final ranking table without recalculation. Order is based on platform mentions, then average listed rank, then best listed rank. Platform mentions count only ranking-discovery mentions, not the number of platforms that later completed a fit assessment.

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

Questions This Section Answers

• Which AI recommendation intelligence platform should a buyer choose if they need to distinguish AI recommendations from simple brand mentions?
• Which AI recommendation intelligence platform is best for an enterprise that needs prompt-volume intelligence and SOC 2 security controls?
• Which AI recommendation intelligence platform should a small team on a tight budget choose for daily multi-engine tracking?

1. Peec AI

Questions This Section Answers

• Is Peec AI worth it for AI recommendation intelligence, and what are its main drawbacks?
• Is Peec AI or OtterlyAI better for tracking AI shopping recommendations when product-level position matters?
• Which Peec AI plan should a buyer choose if they need more than three AI models tracked?

Verdict. Peec AI is the consensus leader for AI recommendation intelligence, named by 4 of 7 platforms with an average listed position of 2.5 and a best position of 1. It is strongest for brands and agencies measuring whether products or brands are recommended in ChatGPT and other AI answer environments, and for teams comparing recommendation coverage, competitive position, cited sources, prompt performance, and changes over time. It is a measurement and recommendation-identification tool, not an execution platform.

Why it ranked here. Peec AI earned the top rank on the strength of 4 platform mentions (57.1% share) and an average listed position of 2.5. It was ranked first by OpenAI and DeepSeek, second by Grok, and sixth by Google. The Peec AI fit review covers the full evidence bundle.

Best suited for. Brands and agencies measuring whether products or brands are recommended in ChatGPT and other AI answer environments; teams comparing recommendation coverage, competitive position, cited sources, prompt performance, and changes over time; e-commerce companies needing product-level AI shopping recommendation tracking; marketing teams that want prioritized opportunity recommendations rather than only a dashboard ^[1].

Main strengths for the use case. Peec AI reports whether individual products appear in AI-generated shopping recommendations, including win rate, position, share of voice, cited-versus-catalog price, and competing products shown alongside them ^[1]. For general AI answers, it separates brand visibility or explicit naming from source visibility and tracks position, sentiment, and share of voice ^[1]. It supports custom prompt management, topic and funnel tagging, AI-suggested prompts based on search-volume data, and a beta Prompt Volume score ^[1]. Competitor comparison uses every detected brand in a response rather than only selected competitors ^[1]. Peec Actions converts visibility and source-gap data into prioritized opportunities across owned pages, editorial coverage, reference sites, and user-generated-content communities ^[2]. All paid plans execute tracked prompts once every 24 hours, which supports week-over-week trend analysis ^[3]. Every plan includes unlimited user seats ^[4].

Main limitations. The documented product-recommendation capability is currently centered on ChatGPT's product carousel; broader platform coverage for product recommendations is not established ^[1]. General brand mentions and recommendation status are related but not identical, so buyers should validate Peec AI's classification rules for recommendation intent in prose answers ^[1]. UI-scraped outputs can change because of model, interface, geography, personalization, sampling, and answer variability ^[1]. Peec AI does not create or publish content ^[1]. Self-serve plans cap included models at three; adding a fourth, fifth, or sixth engine requires per-tier add-ons of $30–$140/month ^[5]. Peec AI does not provide AI traffic estimation or connect citations to website visits or leads ^[7]. Current security certification, API limits, retention, service levels, and enterprise terms are not sufficiently verified ^[1].

Pricing or cost summary. The reviewed Peec AI material lists brand plans at Starter $95/month for 50 prompts and 1 project, Pro $245/month for 150 prompts and 2 projects, Advanced $495/month for 350 prompts and 5 projects, with Enterprise custom pricing ^[1]. Annual billing saves approximately 15% ^[1]. Agency pricing starts at $245/month and scales to $795/month ^[8].

2. OtterlyAI

Questions This Section Answers

• Is OtterlyAI worth it for AI recommendation intelligence, and what are its main drawbacks?
• Which OtterlyAI plan should a buyer choose if they need Claude, Gemini, and Google AI Mode coverage?
• Is OtterlyAI or Peec AI better for a small team that needs the lowest entry price for daily AI visibility tracking?

Verdict. OtterlyAI is a strong second-place option for practical AI recommendation and visibility monitoring across major AI-search platforms, especially for marketing teams that need competitor benchmarking, position and coverage trends, citation analysis, and prioritized actions. It ties Peec AI on platform mentions (4 of 7, 57.1%) but ranks lower on average listed position (4.0). Treat it as an AI-search visibility and recommendation-workflow platform rather than a fully proven recommendation-intelligence or outcome-attribution system.

Why it ranked here. OtterlyAI was ranked third by Grok and Perplexity, and fifth by DeepSeek and Google. Its 4 platform mentions tie the leader, but its average listed position of 4.0 places it second overall. The OtterlyAI fit review covers the full evidence bundle.

Best suited for. Marketing and SEO teams monitoring brand visibility and competitor recommendations in ChatGPT, Google AI Overviews, Perplexity and Microsoft Copilot; SMEs, agencies and teams wanting relatively transparent prompt-volume pricing and daily tracking; buyers that value actionable recommendations, citation-source analysis and competitive content-gap discovery ^[9].

Main strengths for the use case. OtterlyAI reports brand mentions, coverage and position, and its stated use cases include identifying whether a product is suggested in AI-generated answers ^[10]. The platform exposes Brand Coverage, Brand Ranking and position-related reporting, and describes competitors with high coverage but low position ^[11]. Users can add competitors, detect additional brands appearing in tracked answers, and compare share of voice, coverage over time, rankings, domain citations and prompts where competitors appear but the buyer's brand does not ^[9]. It provides daily tracking, coverage-over-time trends and recurring recommendation refreshes ^[13]. Recommendations are categorized by prompts, crawlability, entity, content partnerships, Reddit, news and media, YouTube, social media and content, and can be placed in a To-Do workflow ^[13]. The platform reports domain sources, domain coverage and cited URLs ^[14]. It tracks 50+ countries and languages ^[16].

Main limitations. Recommendation quality is not independently validated in the public materials, and the Recommendations feature is explicitly described as beta ^[13]. Base coverage excludes some relevant models and surfaces unless add-ons or enterprise arrangements are purchased ^[17]. Public documentation does not establish causal attribution to downstream conversions, revenue or actual user recommendations ^[9]. Recommendation refreshes require at least 15 prompts, three competitors and three days of collected data; the Lite plan provides only a preview of up to three recommendations per seven-day cycle ^[13]. Recommendations cannot currently be synchronized with external task-management systems ^[13]. The platform's weekly refresh cycle means monitoring could be up to 7 days behind real-time ^[18]. Sentiment analysis is described as ultra-sensitive, classifying purely factual or neutral mentions as slightly negative ^[19]. OtterlyAI does not create or rewrite content ^[21].

Pricing or cost summary. The current public pricing page lists Lite at $29 per month, Standard at $189 per month and Premium at $489 per month, with annual billing advertised as 15% off ^[17]. Enterprise pricing is custom and starts from $1,000/month ^[22]. Additional prompts are listed at $99 per 100 prompts on Standard ^[17].

3. Profound (tryprofound.com)

Questions This Section Answers

• Is Profound (tryprofound.com) worth it for enterprise AI recommendation intelligence, and what are its main drawbacks?
• Which Profound plan should an enterprise choose if it needs more than three answer engines tracked?
• Is Profound or Peec AI better for a buyer that needs real-user prompt-volume data rather than synthetic prompt lists?

Verdict. Profound is the strongest option on average listed position (1.5) and best position (1), but it appeared on only 2 platforms (28.6% share), which places it third overall. It is well aligned with enterprise AI recommendation intelligence when the buyer values prompt discovery, competitor comparisons, position and share-of-voice analytics, citations, and longitudinal monitoring across multiple answer engines. It is not yet a fully verified strong fit because public evidence does not clearly establish recommendation-specific classification accuracy, and enterprise price and limits are custom.

Why it ranked here. Profound was ranked first by Grok and second by DeepSeek. Its average listed position of 1.5 is the best in the study, but its 2 platform mentions place it behind Peec AI and OtterlyAI. The Profound (tryprofound.com) fit review covers the full evidence bundle.

Best suited for. Enterprise brands and agencies tracking AI recommendations across multiple answer engines; teams that need competitor benchmarking, prompt-level visibility rank, share of voice, citations, and historical monitoring; organizations that value real-user prompt-volume intelligence and enterprise support or security features ^[23].

Main strengths for the use case. Profound tracks whether a brand appears in AI responses and reports visibility, visibility rank, share of voice, sentiment, citations, and competitor presence ^[24]. Its prompt recommendation engine uses millions of real user conversations, and its Profound Index uses more than 1.5 billion real user prompts to identify what people are asking and to surface prompt volumes and co-mention share ^[25]. The platform supports competitor tracking, competitor visibility comparisons, share of voice, co-mention analysis, and citation-source analysis ^[24]. It runs structured prompts daily and analyzes visibility, citations, sentiment, ranking, and competitive presence ^[23]. Enterprise can be configured for up to nine answer engines, including Google AI Mode, Gemini, Microsoft Copilot, Grok, DeepSeek, Claude, and Google AI Overviews ^[23]. Enterprise materials list dedicated Slack support, SSO/SAML, and SOC2 compliance ^[23]. Profound supports 150+ regions and 30+ languages ^[26].

Main limitations. Recommendation-specific classification is not clearly documented as distinct from general brand mentions; the buyer should not assume that visibility equals recommendation inclusion ^[23]. Starter is unsuitable for multi-platform recommendation intelligence because it tracks ChatGPT only ^[23]. Growth covers three engines and 100 prompts, which may be insufficient for large catalogs, many markets, or extensive competitor sets ^[23]. Enterprise pricing, prompt capacity, historical retention, alerting, and data-access limits are not public ^[23]. Company-published feature comparisons and research claims are not equivalent to independent validation of accuracy, representativeness, or causal impact on recommendations ^[23]. Independent reviews report technical instability including duplicated prompts, data restoration issues, and broken tracking after plan changes, plus slow load times of 10–15 seconds ^[28]. Support delays of up to one week and billing problems leading to account freezes are also reported ^[30]. Profound does not connect AI sessions to site visits, page behavior, or revenue ^[32]. Tracking is country-level only, not sub-regional ^[33]. .

4. Scrunch AI

Questions This Section Answers

• Is Scrunch AI worth it for enterprise AI recommendation intelligence, and what are its main drawbacks?
• Which Scrunch AI tier should a buyer choose if they need Claude, Gemini, Meta AI, Google AI Mode, and Grok coverage?
• Is Scrunch AI or Profound better for a buyer that needs CDN-level optimization of content served to AI crawlers?

Verdict. Scrunch AI is a credible shortlist candidate for enterprise AI recommendation intelligence because its Enterprise offering combines prompt-level visibility, competitor comparison, citations, recommendation position metrics, shopping-result analysis, historical monitoring, API access, and governance features. It ranked fourth with 2 platform mentions (28.6% share) and an average listed position of 5.0. Procurement should remain conditional on validating prompt methodology, recommendation-versus-mention classification, measurement coverage, refresh behavior, and the quoted Enterprise commercial terms.

Why it ranked here. Scrunch AI was ranked fourth by Grok and sixth by DeepSeek. Its average listed position of 5.0 places it fourth overall. The Scrunch AI fit review covers the full evidence bundle.

Best suited for. Enterprise brands monitoring AI recommendations across multiple generative-answer platforms; teams needing competitor visibility, prompt drill-downs, citation tracking, and historical change monitoring; organizations wanting API access, integrations, SSO, custom workspaces, and dedicated account support ^[34].

Main strengths for the use case. Scrunch reports product recommendations in AI shopping results, classifies products as the buyer's brand, competitors, or third parties, and exposes product counts by prompt, observations, price ranges, ratings, average shelf position, retailers, and observation dates ^[35]. The Shopping view includes position trends and First Position Rate, defined as how often a product appears as the number-one recommendation ^[35]. Scrunch also reports brand and competitor presence, position, sentiment, and citations across AI platforms ^[36]. It claims automated competitor visibility monitoring, filters by time period, competitor, topic, persona, and funnel stage, prompt-level competitor drill-downs, citation comparison, gap analysis, and Suggested Competitors ^[37]. Enterprise publicly lists nine platforms: ChatGPT, Claude, Perplexity, Gemini, Meta AI, Google AI Mode, Google AI Overviews, Copilot, and Grok ^[38]. Enterprise includes custom prompt volumes, custom workspaces and user licenses, API access and integrations, SAML/OIDC SSO, complete site audits, an AXP offering, and a dedicated account team ^[34]. The Agent Experience Platform detects AI agent traffic at the CDN layer and serves optimized, LLM-ready content ^[39].

Main limitations. Enterprise pricing and commercial terms are not public ^[34]. Core's four-model coverage may be insufficient for buyers needing Claude, Gemini, Meta AI, Google AI Mode, or Grok without Enterprise ^[38]. Public documentation does not clearly define sampling, refresh frequency, geographic/session variation, prompt-generation methodology, or statistical confidence ^[34]. Shopping metrics depend on AI platforms returning shopping-result structures; they do not cover every textual recommendation ^[35]. Public evidence is primarily vendor-reported, with limited independent validation of measurement accuracy or business outcomes ^[34]. Scrunch is not a recommendation generation engine; it optimizes and monitors recommendations created by third-party LLMs ^[40]. AXP requires Cloudflare, Akamai, or AWS CloudFront integration ^[39]. The normalization stage reported conflicting official domains and an unresolved identity fallback ^[34].

Pricing or cost summary. Core is publicly listed at $250 per month for brands, with a 7-day free trial ^[34]. Enterprise is custom-priced and requires a sales engagement or quote ^[34]. An independent review documents a Growth tier at $417/month on annual billing ($500 month-to-month) and additional seats at $25/month ^[41]. Annual billing receives a 17% discount ^[42].

5. Ahrefs

Questions This Section Answers

• Is Ahrefs Brand Radar worth it for AI recommendation intelligence, and what are its main drawbacks?
• Which Ahrefs Brand Radar configuration should a buyer choose if they need all six AI platform indexes?
• Is Ahrefs Brand Radar or Peec AI better for a team that already pays for SEO tooling and wants AI visibility in the same platform?

Verdict. Ahrefs Brand Radar is a mixed fit. It is strong for broad AI visibility discovery, competitor benchmarking, prompt monitoring, citation/source analysis, and trend reporting. It is not yet a clearly strong standalone choice for recommendation intelligence if the buyer's defining requirements are explicit recommendation-versus-mention classification and recommendation position measurement, because those capabilities were not verified in the public documentation. It ranked fifth with 2 platform mentions (28.6% share) and an average listed position of 5.5.

Why it ranked here. Ahrefs was ranked third by DeepSeek and eighth by Google. Its average listed position of 5.5 ties Loamly, but its best position of 3 places it fifth. The Ahrefs fit review covers the full evidence bundle.

Best suited for. Broad AI-search visibility discovery across major AI assistants and Google AI results; competitor share-of-voice, mention, citation, prompt, and source-domain monitoring; teams already using Ahrefs for SEO and wanting AI visibility data in the same platform ^[43].

Main strengths for the use case. Brand Radar tracks AI visibility across Google AI Overviews and AI Mode, ChatGPT, Perplexity, Gemini, Copilot, Grok, and Claude, subject to platform-specific availability and collection conditions ^[43]. The AI Visibility Index analyzes hundreds of millions of search-backed prompts modeled from Ahrefs keyword data ^[44]. Custom Prompts allow buyers to track exact buyer questions and can generate prompt suggestions from competitor comparisons, pricing data, and related signals ^[44]. Brand Radar supports benchmarking AI share of voice against competitors, configuring brand and competitor entities, and comparing mentions, citations, impressions, and related visibility metrics ^[45]. Saved reports can be revisited for current results, custom prompts can run daily, weekly, or monthly, and Brand Radar data can be used in reports, API workflows, and Looker Studio ^[46]. Brand Radar identifies top cited pages and domains and provides citation-related reporting ^[43]. Ahrefs states that prompts are entered in the respective AI platforms using the location associated with the source keyword and that responses are captured without stored user data, prior context, normalization, personalization, pre-prompting, or filtering ^[43].

Main limitations. Brand Radar defines a mention as a brand appearing at least once in an AI-generated response; the checked documentation does not establish a separate, validated metric that distinguishes an explicit recommendation from a neutral mention, comparison inclusion, criticism, or other answer role ^[45]. The checked sources do not document a standardized metric for the position of a recommendation within an AI answer or a recommendation rank among alternatives ^[43]. The AI Visibility Index is based on Ahrefs search-backed prompts and modeled demand, not necessarily the buyer's complete real-world conversation set ^[43]. Grok data collection may be temporarily unavailable because of policy changes, while Claude custom checks consume eight checks per update ^[43]. Independent testing documents significant accuracy gaps: one reviewer reported Brand Radar showing 3 ChatGPT mentions globally versus an actual count of 123—a 97.5% discrepancy ^[47]. Another review reported a 73% overall accuracy rate ^[48]. Brand Radar does not provide sentiment analysis of mentions or analyze whether AI recommendations are positive or misrepresentative ^[49].

6. Loamly

Questions This Section Answers

• Is Loamly worth it for AI recommendation intelligence, and what are its main drawbacks?
• Which Loamly product should a buyer choose first: the one-time Intelligence Report or the recurring monitoring plan?
• Is Loamly or Profound better for a buyer that needs forensic citation tracing rather than high-volume prompt tracking?

Verdict. Loamly is unusually aligned with recommendation-intelligence requirements because it claims to separate recommendations from mentions, trace influential citations, compare competitors, test position stability, and monitor change over time. It ranked sixth with 2 platform mentions (28.6% share) and an average listed position of 5.5. The main buying risks are incomplete public scoring details, custom pricing for the deepest analysis, a difference between six-platform report coverage and four-platform recurring monitoring, and limited independent evidence.

Why it ranked here. Loamly was ranked fifth by Kimi and sixth by Perplexity. Its average listed position of 5.5 ties Ahrefs, but its best position of 5 places it sixth. The Loamly fit review covers the full evidence bundle.

Best suited for. Marketing, SEO, content, and strategy teams that need to distinguish AI recommendations from simple brand mentions; companies needing competitor comparisons, source-level diagnosis, high-value buyer-query analysis, and a prioritized remediation plan; buyers wanting a combined one-time forensic audit and lower-cost ongoing monitoring product ^[50].

Main strengths for the use case. Loamly explicitly distinguishes being cited or mentioned from being recommended and states that its analysis identifies the sources that drive recommendations ^[50]. The Intelligence Report measures visibility across 50+ buyer queries and reports platform-by-platform visibility, and claims position-stability testing across dozens of scenarios ^[51]. The Category Snapshot includes a competitive positioning matrix, while the full report includes competitor-position stress testing and identifies where competitor displacement may be feasible ^[51]. Loamly states that its query set includes generic, use-case, competitor, buying-intent, and adversarial queries ^[52]. The Grow plan includes 50 daily prompts, four named AI platforms, competitor intelligence, API access, CSV export, and two years of data retention; Pro increases prompt and event limits and provides unlimited retention ^[53]. The full report includes raw CSVs containing queries, responses, citations, and sub-queries, plus a 90-day playbook and walkthrough call ^[51]. Loamly detects dark AI traffic and integrates with Stripe for revenue tracking ^[54]. It offers an open-source MIT-licensed tracker and states GDPR compliance with no cookies and EU hosting available ^[56].

Main limitations. The Full Intelligence Report is custom-priced, limiting direct purchase comparability ^[53]. Recurring monitoring publicly covers four platforms, while the full report names six; coverage needs confirmation for the buyer's required platforms ^[53]. Exact definitions of recommendation, position, visibility rate, competitor share, and statistical confidence are not publicly detailed ^[50]. Most performance and ROI evidence on the website is company-reported; independent validation was not identified in the reviewed sources ^[50]. The platform appears oriented toward web-based AI traffic, citations, and answer visibility; support for non-web recommendation surfaces is unclear ^[50]. Monitoring pricing above the entry tier is opaque; no mid-tier or enterprise tiers are publicly listed ^[57]. Position stability claims of a 73% hold rate for early movers and a 12% breakthrough rate for late entrants are Loamly-generated with no third-party data to cross-check ^[58]. Limited independent customer outcomes are published ^[59].

Pricing or cost summary. Loamly offers a one-time Category Snapshot at $990 and prices the Full Intelligence Report by custom scope ^[53].

7. HubSpot AEO

Questions This Section Answers

• Is HubSpot AEO worth it for AI recommendation intelligence, and what are its main drawbacks?
• Which HubSpot AEO plan should a buyer choose if they need more than 25 tracked prompts?
• Is HubSpot AEO or OtterlyAI better for a HubSpot customer that wants CRM-informed prompt suggestions?

Verdict. HubSpot AEO is a mixed fit. It is a strong entry-level option for low-cost, ongoing visibility and competitor monitoring across ChatGPT, Gemini, and Perplexity, especially for HubSpot customers. It is not a fully verified match for advanced AI Recommendation Intelligence because public materials do not establish recommendation-versus-mention classification, recommendation-position measurement, broad platform coverage, or large-scale monitoring. It ranked seventh with 2 platform mentions (28.6% share) and an average listed position of 6.5.

Why it ranked here. HubSpot AEO was ranked fourth by OpenAI and ninth by DeepSeek. Its average listed position of 6.5 is the lowest in the study. The HubSpot AEO fit review covers the full evidence bundle.

Best suited for. Companies starting AEO monitoring with a small prompt set; HubSpot customers wanting CRM-informed prompt suggestions and integrated content recommendations; marketing teams focused on ChatGPT, Gemini, and Perplexity ^[60].

Main strengths for the use case. HubSpot AEO provides prioritized content recommendations based on citation patterns across tracked prompts ^[60]. It tracks prompts daily and reports prompt coverage, brand visibility, trends, and prompt performance over time ^[60]. The product supports competitor visibility, share-of-voice comparison, competitor presence analysis, and comparisons of citation patterns and owned-domain citation rates ^[60]. AEO reports top domains, citation channels, owned-domain citation rate, brand-mention rate in citations, content types, and competitor citation gaps ^[60]. Marketing Hub Professional and Enterprise can generate prompts using business context, CRM data, products, audiences, and ideal customer profiles ^[60]. The free AI Search Grader provides a one-time snapshot without an account ^[61]. HubSpot AEO is the only platform in this category built natively inside a CRM ^[62].

Main limitations. Only three monitored answer engines are publicly documented: ChatGPT, Gemini, and Perplexity ^[60]. The standalone AEO plan and Marketing Hub Professional support 25 daily prompts, 3 engines, and 2,500 answers per month; Marketing Hub Enterprise supports 50 daily prompts, 3 engines, and 5,000 answers per month ^[60]. Public documentation does not clearly verify a separate metric distinguishing an AI recommendation from a simple mention ^[60]. Public feature descriptions do not verify measurement of recommendation rank, answer position, or first-choice placement ^[60]. HubSpot labels AEO as beta, and HubSpot states that initial visibility data is directional and becomes more stable after several weeks of consistent tracking ^[60]. The free Grader is a one-time snapshot rather than continuous intelligence ^[60]. HubSpot AEO is not designed for agencies managing multiple client stacks ^[63]. It does not track Google AI Overviews natively at any price ^[64]. It generates draft recommendations but requires human review before publishing ^[65].

Pricing or cost summary. The free AI Search Grader provides a one-time snapshot without an account ^[61]. HubSpot AEO is publicly listed at $50 per month or $45 per month when paid annually, with a 28-day free trial and no separate HubSpot subscription required ^[66]. Marketing Hub Professional is publicly listed at $800/month and Marketing Hub Enterprise at $3,600/month on HubSpot's pricing page ^[67]. Additional prompt capacity is available as an add-on, but public documentation does not state the complete add-on price schedule ^[67].

What the Cross-Platform Study Reveals About This Market

Questions This Section Answers

• What does the cross-platform study reveal about how AI recommendation intelligence platforms are positioned in 2026?
• Which AI recommendation intelligence platforms are named most consistently across OpenAI, Anthropic, DeepSeek, Grok, Perplexity, Kimi, and Google?

The study reveals a market split between two product philosophies. The first is AI-search visibility monitoring that tracks mentions, citations, share of voice, and position across answer engines. Peec AI, OtterlyAI, Ahrefs, and HubSpot AEO sit primarily in this camp ^[68]. The second is recommendation-specific diagnosis that attempts to separate an explicit recommendation from a neutral mention and trace the third-party sources that drive recommendations. Loamly and, to a lesser degree, Profound and Scrunch AI sit in this camp ^[72].

The study also reveals that recommendation-versus-mention classification is the single most contested capability. OpenAI, DeepSeek, Perplexity, and Kimi all flagged that public documentation does not clearly verify a dedicated recommendation classifier for Peec AI, OtterlyAI, Profound, Scrunch AI, Ahrefs, and HubSpot AEO ^[75]. Only Loamly's own materials explicitly claim this distinction ^[72].

A third pattern is that pricing transparency varies widely. Peec AI, OtterlyAI, Ahrefs, Loamly, and HubSpot AEO publish self-serve entry pricing, while Profound and Scrunch AI gate their full multi-engine tiers behind custom Enterprise quotes ^[68].

A fourth pattern is that engine coverage is the most common gating mechanism. Peec AI caps self-serve plans at three models ^[83].

Where the AI Platforms Agreed

Questions This Section Answers

• Where did OpenAI, Anthropic, DeepSeek, Grok, Perplexity, Kimi, and Google agree about AI recommendation intelligence platforms?

Platforms agreed that Peec AI and OtterlyAI are the two most frequently named options, each appearing on 4 of 7 platforms ^[84]. They agreed that daily tracking cadence matters because AI answers vary day to day ^[86]. They agreed that citation and source analysis is a core differentiator across Peec AI, OtterlyAI, Profound, Scrunch AI, Ahrefs, and Loamly ^[88]. They agreed that competitor share-of-voice comparison is a standard capability ^[84]. They agreed that most platforms do not provide direct revenue attribution from AI recommendations ^[96]. They agreed that UI scraping or web-interface monitoring introduces variability compared with API-only collection ^[84].

Where the AI Platforms Disagreed

Questions This Section Answers

• Where did the AI platforms disagree about AI recommendation intelligence platforms, and what should buyers verify?

The sharpest disagreement was about whether Peec AI, OtterlyAI, Profound, Scrunch AI, Ahrefs, and HubSpot AEO actually distinguish recommendations from simple mentions. OpenAI, DeepSeek, Perplexity, and Kimi said this is unclear or unverified for most of these platforms ^[101]. Grok and Google were more willing to treat visibility and position metrics as proxies for recommendation intelligence ^[107].

The second disagreement was about entity verification. Kimi reported that Peec AI's official website could not be retrieved and rated it uncertain ^[109]. Kimi also reported that Scrunch AI's verified product category does not align with the buyer's need ^[110]. Kimi reported zero direct information about Loamly ^[111]. Other platforms rated these same entities good or strong ^[112].

The third disagreement was about pricing accuracy. Ahrefs pricing conflicts across its own pages: one says Brand Radar starts at $199/month, while the FAQ says standalone from $50/month ^[115]. Peec AI pricing conflicts across sources: some list $95/$245/$495 monthly, while others report lower annual-billed figures ^[116]. Loamly pricing conflicts: the site shows both $29/month platform pricing and one-time audit prices from $299 to $2,490 ^[117].

The fourth disagreement was about fit ratings. Peec AI received strong, good, mixed, and uncertain ratings across platforms ^[112].

How Buyers Should Choose

Questions This Section Answers

• What should a buyer check before choosing an AI recommendation intelligence platform for tracking which brands AI systems recommend?
• Which AI recommendation intelligence platform should a buyer choose if they need to verify recommendation-versus-mention classification before purchase?

Buyers should start by defining whether they need recommendation-specific intelligence or AI-search visibility monitoring. If the core requirement is distinguishing an explicit recommendation from a neutral mention, Loamly is the only platform in this study whose own materials explicitly claim that distinction ^[119]. If the core requirement is broad visibility, competitor benchmarking, and citation analysis, Peec AI and OtterlyAI are the most frequently named options ^[120].

Buyers should then verify engine coverage against their required platforms. Peec AI, OtterlyAI, Profound, Scrunch AI, and HubSpot AEO all gate full multi-engine coverage behind higher tiers or add-ons ^[122]. Ahrefs gates AI indexes behind add-on pricing ^[127].

Buyers should request a proof of concept using their own recommendation prompts and compare each platform's classifications against human judgments. OpenAI specifically recommended this for Profound ^[124]. Buyers should also verify pricing, contract terms, data retention, API limits, and security certifications directly with each vendor, because public documentation is incomplete or conflicting for every platform in this study.

Buyers should treat platform recommendations as market intelligence, not independent customer reviews or proof of quality. The study used one standardized prompt sent once to each included platform, and AI answers can vary by date, wording, location, account state, model, interface, browsing configuration, and the sources retrieved.

Methodology

This study 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 recommendation intelligence platforms the platform would recommend for a company that needs to distinguish recommendations from simple mentions, measure recommendation coverage and position, compare competitors, identify high-value prompts, and track changes over time.

The research date was 2026-09-18. The ranking unit was software platform or research platform. The target buyer was companies seeking AI Recommendation Intelligence Platforms across AI search, generative-answer, and recommendation platforms. The geography was the United States.

Eligibility required an entity to be named by at least two platforms during ranking discovery. Of 49 unique entities named, 7 qualified. The final ranking table is the sole authority for rank, platform mentions, platform share, average rank, and best rank. Platform mentions count only ranking-discovery mentions, not the number of platforms that later completed a fit assessment.

Entity evidence bundles were the authority for buyer fit, features, pricing, strengths, limitations, disagreements, and citations. Citations are platform-reported evidence, not independently verified facts. The URLs were collected from platform responses and were not independently validated by either writer stage.

Methodology Limitations

This 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 variability.

Platform-reported research dates differ from the authoritative run date. DeepSeek's response carried a 2026-06-15 date for Peec AI, 2026-01-15 for OtterlyAI, 2026-02-14 for Profound and Scrunch AI, and 2026-02-14 for Ahrefs, Loamly, and HubSpot AEO. These are provenance metadata and do not independently prove freshness.

Company-owned citations materially outnumber independent citations for several entities. OtterlyAI's evidence bundle contains 27 owned and 19 independent citations. Profound's contains 22 owned and 30 independent. Scrunch AI's contains 25 owned and 20 independent. Ahrefs's contains 24 owned and 18 independent. Loamly's contains 25 owned and 10 independent. HubSpot AEO's contains 22 owned and 14 independent. Peec AI's contains 14 owned and 23 independent. Company claims should not be described as independently verified.

The deterministic identity audit contains qualification notes that must be disclosed. Official-site retrieval failed for one or more mentions of Peec AI, Profound, Scrunch AI, and Ahrefs. Scrunch AI's normalization stage reported conflicting official domains and an unresolved identity fallback. Peec AI's identity used exact-name fallback, and the matching reported domain remains unverified.

Platform recommendations are market intelligence, not independent customer reviews or proof of quality. No-search model claims require explicit verification before being described as current facts. Conflicting product names, pricing, or capabilities were not resolved by guessing; conflicts are described and buyers are told what to verify.

Final Verdict

Peec AI is the consensus leader for AI recommendation intelligence, named by 4 of 7 platforms with an average listed position of 2.5 and a best position of 1. It is strongest for brands and agencies measuring whether products or brands are recommended in ChatGPT and other AI answer environments, and for e-commerce companies needing product-level AI shopping recommendation tracking.

OtterlyAI ties on platform mentions and is the strongest alternative for low-cost daily multi-engine monitoring, with a $29/month entry point and four base engines. Profound has the best average listed position (1.5) and is the strongest alternative for enterprise prompt-volume intelligence and security, but its full multi-engine coverage requires custom Enterprise pricing. Scrunch AI is the strongest alternative for enterprise governance with CDN-level agent optimization. Ahrefs is the strongest alternative for teams already using Ahrefs for SEO. Loamly is the strongest alternative for forensic recommendation-versus-mention diagnosis. HubSpot AEO is the strongest alternative for entry-level AEO monitoring for HubSpot customers.

No platform in this study has independently validated recommendation-versus-mention classification across all supported engines. Buyers should verify that capability directly with each vendor before purchase.