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LLM Pulse White-Label AI Visibility Platform Fit Review for Agencies

LLM Pulse is a good fit for agencies that want to resell AI visibility monitoring under their own brand, but it is not a fully verified one.

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

Answer Capsule

LLM Pulse is a good fit for agencies that want to resell AI visibility monitoring under their own brand, but it is not a fully verified one. Four of the seven platforms in this study named LLM Pulse during ranking discovery, and it finished first overall with an average listed rank of 2.75. Its strongest asset is a purpose-built white-label stack: Partial White-Label, Full White-Label with a custom domain, and iframe Embed Integration, plus multi-client dashboards and unlimited seats. The main limitation is commercial opacity: full white-label pricing is quote-based, public pricing is in euros, and independent validation of accuracy, uptime, and compliance is thin.

Research Snapshot

FieldValue
Platform mentions in ranking stage4 of 7 included platforms
Share of included platform responses57.1%
Average listed rank2.75
Best listed rank1
Relevant product/model/planFull Whitelabel, Partial Whitelabel, and Embed Integration options for agencies; Enterprise/Partner plan for custom volume and white-label functionality
Overall use-case fitGood (platform fit ratings: strong from Anthropic, Google, and Grok; good from OpenAI, Kimi, and Perplexity; uncertain from DeepSeek)
Research date2026-09-19

Why LLM Pulse Qualified for This Study

Questions This Section Answers

  • Is LLM Pulse a good choice for white-label AI visibility platforms for agencies?
  • How often did AI platforms recommend LLM Pulse for agency white-label delivery?

LLM Pulse qualified because it was named by four of the seven platforms that produced ranking-stage responses — Anthropic, Grok, Kimi, and Perplexity — which is 57.1% of included platform responses. It ranked first on Grok and Kimi, third on Anthropic, and sixth on Perplexity, producing an average listed rank of 2.75 and a best rank of 1. It also finished first in the final ranking for this use case.

Qualification was not unanimous. OpenAI, Google, and DeepSeek evaluated LLM Pulse's fit but did not name it during ranking discovery, so their assessments appear in the fit sections rather than the mention count. DeepSeek's response is also the weakest evidence in the set: it ran without search enabled, its research date is 2026-01-15 rather than the study date of 2026-09-19, and it rated fit as uncertain because it could not confirm plan details, contract terms, or independent reviews [1].

The entity is a company, not a product line, and the relevant offer for this use case is its agency white-label stack. LLM Pulse is a European vendor that lists prices in EUR and states it has raised no outside capital (official:C1). That profile matters for US agencies weighing vendor stability, procurement paperwork, and currency exposure.

The Product, Model, Plan, or Service Most Relevant to White-Label AI Visibility Platforms for Agencies

Questions This Section Answers

  • Which LLM Pulse plan should an agency choose if it needs a custom domain with no vendor branding?
  • Does LLM Pulse offer an embedded dashboard option for agencies that already have a client portal?

The most relevant offer is the white-label tier structure rather than a single named plan. LLM Pulse describes three levels: Partial White-Label, which uses a branded subdomain and retains a "Powered by LLM Pulse" footer; Full White-Label, which uses a custom domain with no LLM Pulse branding visible to clients; and Embed Integration, which places dashboards inside an existing portal through a secure iframe with JWT authentication [2].

For agencies that need a client-facing product on their own domain, Full White-Label is the clearest match. For agencies or SaaS providers that already operate a client portal, Embed Integration is the better structural fit, though the public description limits each embed instance to a single project view, which may require additional implementation for a broad client portfolio [2].

Plan naming is genuinely inconsistent across sources. The ranking-stage responses refer to "Agency plans with white-label features," an "Agency solution," an "Agency/White-Label Plan," and "Full Whitelabel or Embed Integration options." Company pages describe Partial Whitelabel, Full Whitelabel, Embed Integration, and Enterprise, while the pricing page also references Partner plans starting from 25 client projects and 3,600 tracked prompts [6]. Buyers should treat the exact commercial name of the agency white-label package as unconfirmed and ask for it in writing.

What the AI Platforms Agreed About

Questions This Section Answers

  • What do AI platforms agree LLM Pulse does well for agency white-label reporting?
  • Is LLM Pulse built specifically for agency resale rather than general brand monitoring?

The strongest area of agreement is that LLM Pulse is purpose-built for agency resale rather than adapted to it. Multiple platforms describe explicit white-label tiers, multi-client or multi-project dashboards, and unlimited seats on standard plans [8].

Platforms also agreed on the core monitoring feature set relevant to agency retainers: prompt tracking, citation-source analysis, brand mentions, sentiment, competitor benchmarking, share of voice, and recommendations [14]. Citation analysis showing which sources AI models cite is reported as available from the entry plan upward [16].

Reporting and integration breadth drew consistent support. Sources describe a Looker Studio connector with a ready-made template, CSV and Excel exports, REST API access, a Rust CLI, and a hosted MCP server, with API and CLI access beginning on the Scale tier [19]. Agencies can connect Looker Studio once and duplicate templates per client so dashboards refresh before monthly calls [23].

A fourth point of agreement is that agencies are a core customer segment. LLM Pulse states agencies are one of its largest customer segments and that agency resources include training materials, sales decks, priority support, and dedicated account management, though that support claim is platform-reported with only partial support strength [24].

Where the AI Platforms Disagreed or Were Uncertain

Questions This Section Answers

  • Where do AI platforms disagree about LLM Pulse's white-label pricing and plan limits?
  • Is LLM Pulse's SOC 2 compliance status confirmed or unverified?

Pricing is the sharpest disagreement. OpenAI reports Starter at €49, Growth at €99, and Scale at €299 per month [26]. Anthropic reports a longer ladder including Scale+ at €599 and Scale++ at €1,199 per month [27]. Grok reports USD figures of roughly $96, $180, and $542 per month for comparable tiers [28]. Google reports a top portfolio tier at €5,999 per month, or €4,999.17 billed annually, for 10,000 prompts and 35 projects [29]. Perplexity notes that public sources conflict on whether the entry plan is €49 or roughly $54–$60 equivalent [30]. The company's own pricing page lists EUR amounts with daily-refreshed conversions and states that Stripe charges the card in local currency at the live FX rate plus a conversion fee (official:C2).

Prompt and project limits also conflict. OpenAI and Anthropic describe Starter at 50 tracked prompts and one project [26]. Google reports Starter at 40 tracked prompts [31]. The company's pricing page states that one tracked prompt covers all five included models and counts once against the plan limit regardless of how many models it runs against (official:C2).

Compliance status is a third area of uncertainty. An independent review states LLM Pulse does not currently offer SOC 2 Type II certification and that this is unverified [32]. A company-authored comparison page states LLM Pulse does not currently offer SOC 2 Type II while naming Profound as certified [33]. No source in this study shows a completed SOC 2 attestation.

Model coverage is a fourth. All public plans include five core models — ChatGPT, Perplexity, Gemini, Google AI Mode, and Google AI Overviews — while Claude, Copilot, Grok, DeepSeek, and others are paid add-ons or Enterprise-only [34]. One platform reported the site advertising 14 AI models while standard plans show five included, leaving the incremental cost and availability of additional models unclear [26].

Tracking cadence drew mixed treatment. Several sources note weekly tracking as the default, with daily tracking available as a separate plan option on every tier [31]. One platform reported that daily tracking plans forfeit the free trial, increasing upfront commitment risk [37].

Use-Case-Specific Features and Capabilities

Questions This Section Answers

  • Does LLM Pulse support multi-client management and competitor comparisons for agency retainers?
  • What export and integration options does LLM Pulse provide for client reporting?

Multi-client management is the core agency capability. Sources describe separate projects per client from a single account, the ability to switch between brands and compare performance, and a multi-client dashboard with role assignment and data separation [38]. Unlimited seats are reported as standard across plans, which removes per-seat cost friction as an agency adds team members and client stakeholders [40].

Competitor and citation tracking are reported as available on Growth and above. Competitive landscape comparisons let agencies benchmark client brands against competitors across tracked models, and sentiment analysis indicates whether AI describes a brand positively, neutrally, or negatively [41]. Custom competitor sets of 10 to 25 competitors per project are described in one source [43].

Reporting and export options span several formats. Sources describe a Looker Studio connector with a ready-made template, CSV and Excel exports with tagging and annotations, REST API access to metrics including timeseries, share of voice, and citations, a Rust CLI, and a hosted MCP server [44]. API and CLI access begin on the Scale tier, while Looker Studio and CSV exports are described as available on all plans [44].

Embedded client portals use iframe deployment with JWT authentication, domain allowlisting, customizable colors and fonts, PostMessage communication, and real-time synchronization with the parent platform [47]. One source describes automated DNS and SSL configuration in the embed workflow [48].

The LLM Pulse Agent, a conversational copilot for querying data and taking actions, is available on Growth and above but not on the entry Starter plan [49]. Sentiment analysis is likewise reported on Growth and above, not on Starter [41].

Pricing, Fees, Contracts, and Ongoing Costs

Questions This Section Answers

  • How much does LLM Pulse cost per month for an agency white-label plan, and is full white-label pricing published?
  • What contract, cancellation, and currency-conversion terms apply to LLM Pulse agency subscriptions?

Public self-serve pricing is published in EUR, but full white-label pricing is not. The company's pricing page lists Starter at €49, Growth at €99, and Scale at €299 per month, with annual billing described as two months free, or roughly 17% savings, and yearly billing costing ten times the monthly price [50]. Anthropic reports additional tiers at €599 and €1,199 per month [51]. Google reports a top portfolio tier at €5,999 per month or €4,999.17 billed annually [52]. Grok reports USD equivalents of roughly $96, $180, and $542 per month [53].

White-label and Enterprise pricing is quote-based. The company states that Partner plans bundle multi-client capacity with white-label delivery, volume pricing, and a dedicated onboarding team, starting from 25 client projects and 3,600 tracked prompts [54]. One platform reported that full white-label requires Enterprise with custom pricing and sales contact [53]. Another reported that white-label is not included in Starter, Growth, or Scale but is included in Enterprise [55].

Add-on costs are partially published. Extra AI models are priced per model and per tier, with the company stating extra model prices start at the Starter weekly rate (official:C2). Google reports add-ons of €100 per month for 100 tracked prompts, €50 per month for one project, and from €10 per month per extra AI model [52]. OpenAI reports that white-label, custom-domain, embedded-dashboard, API-volume, and support charges are not publicly itemized [50].

Contract terms are comparatively clear for self-serve and unclear for Enterprise. The company's terms state that subscriptions are billed in advance monthly or annually, renew automatically for successive equal periods unless cancelled before the renewal date, and that cancellation takes effect at the end of the current billing period with access retained until then (official:C3). A 14-day free trial requires a valid payment method and converts to a paid subscription unless cancelled before it ends [56]. The company states it may change plan prices with at least 30 days' email notice for self-serve subscriptions, applying from the next renewal (official:C3). Enterprise Orders can modify terms including custom pricing, volumes, service levels, security commitments, and invoice billing (official:C3).

Currency is a real cost variable for US buyers. Prices are listed in EUR and converted at daily-refreshed reference rates, but Stripe charges the card in local currency at the live FX rate plus a small conversion fee, so the final amount can differ from the displayed figure (official:C2). Prices are exclusive of VAT and local taxes (official:C2). One platform flagged that EUR-denominated pricing without a fixed USD option introduces exchange-rate variance risk for US agencies [51].

Data portability terms are documented. The company states that customers can export data at any time during the subscription using export features and, on eligible plans, the API, and that switching assistance and data egress are free of charge under the EU Data Act, with a notice period not exceeding two months and transition completed within 30 calendar days (official:C3). After termination, the company states it will make a reasonable export of customer data and reports available on request for 30 days and delete customer data within 90 days, except for backups and legally required retention (official:C3).

Best Suited For

Questions This Section Answers

  • Which type of agency gets the most value from LLM Pulse white-label plans?
  • Is LLM Pulse a good fit for agencies bundling AI visibility into existing SEO retainers?

LLM Pulse is best suited to small and mid-sized agencies launching AI visibility, GEO, or AEO services as a new revenue line, particularly those bundling the service with existing SEO or PR retainers. One platform reports agencies charging $500–$2,000 per month per client for AI visibility monitoring and reporting, which frames the resale economics [57].

It also suits agencies that need branded multi-client dashboards rather than downloadable reports alone, and agencies or SaaS providers that need embedded dashboards through iframe and JWT authentication [58]. Teams that value transparent self-serve pricing, rapid setup, and practical integrations such as Looker Studio, API, MCP, and CSV export over enterprise compliance infrastructure are a strong match [59].

Consultants and resellers who want to offer AI visibility without sales friction or annual contract minimums fit the self-serve model, since month-to-month billing is available on self-serve plans and no mandatory annual contract is required [62]. Agencies already comfortable with EUR-denominated billing and Stripe currency conversion are better positioned than those requiring fixed USD pricing (official:C2).

Probably Not Best Suited For

Questions This Section Answers

  • Who should not choose LLM Pulse for white-label AI visibility delivery?
  • Is LLM Pulse unsuitable for agencies with hard SOC 2 or enterprise procurement requirements?

Agencies with hard compliance requirements should look elsewhere. No source in this study shows a completed SOC 2 Type II attestation, and an independent review states LLM Pulse does not currently offer it [63]. Organizations requiring HIPAA or GDPR verification as procurement prerequisites, or requiring independently audited evidence of measurement accuracy, uptime, or enterprise compliance before purchase, are not well served by the available evidence [63].

Agencies tracking hundreds of prompts across dozens of clients without moving to custom Enterprise pricing will hit self-serve ceilings. Scale++ is reported to cap at 15 projects and 2,200 prompts, pushing multi-client agencies to Enterprise with unspecified costs [66]. One platform notes that public plan limits may be restrictive for agencies with many clients or high prompt volumes unless Enterprise capacity is purchased [65].

Buyers who need Claude, Grok, Copilot, or other long-tail models as standard features will not find them on public plans; they are add-ons or Enterprise-only [68]. Buyers wanting a USD-denominated pricing ladder without currency conversion variance are also a weaker fit [66].

Small agencies needing only one or two inexpensive client projects and no white-label presentation are over-served by this stack [65]. Procurement processes that weight vendor size, third-party review volume, or established enterprise reference customers may also find LLM Pulse a harder sell, given its bootstrapped status and limited public case-study footprint [63].

When Another Option May Be Better

Questions This Section Answers

  • What is a better alternative to LLM Pulse for an agency that needs SOC 2 Type II compliance?
  • When should an agency choose a different white-label AI visibility platform over LLM Pulse?

Choose a platform with public agency pricing when predictable per-client margin calculations matter more than custom branding flexibility [71]. Choose a platform with independently documented accuracy, uptime, compliance, and data-processing terms when procurement or enterprise governance is stringent [71].

For SOC 2 Type II as a non-negotiable procurement requirement, one platform names Profound as the established alternative, noting it is SOC 2 Type II certified and HIPAA compliant with higher pricing starting at $99 in USD [73]. For agencies tracking hundreds of prompts across 20 or more clients, the same source names Scale++ at €1,199 per month with 2,200 prompts as the highest self-serve cap, and suggests Trakkr or Profound may offer better scaling without prompt constraints [74].

For buyers who need Claude, Grok, or other long-tail models as standard rather than add-on, one platform notes Trakkr lists all eight AI models on every plan while LLM Pulse reserves them as Enterprise add-ons [74]. For USD-denominated fixed pricing without daily currency conversion, the same source cites Trakkr's published fixed USD pricing at $100 Growth and $500 Scale [74].

For agencies needing clear self-serve pricing under $200 per month, one platform names Ayzeo Pro at $149 per month or AgenticLens at $199 per month as alternatives [75]. For integrated SEO and GEO audit tools, keyword research, or AI content generation alongside visibility tracking, the same source names Georion at $499–$1,498 per month [77]. For prospecting and pitch-scan capabilities, Routeless Radar at $499 per month includes 20 fast and 10 deep pitch scans [78]. For immediate daily monitoring with a trial period, AgenticLens offers daily monitoring at $199 per month with cancel-anytime terms [76].

For agencies needing daily tracking frequency and immediate brand hallucination alerts, one platform suggests a platform like GEO Metrics may fit better [79]. For built-in, search-volume-backed prompt discovery metrics natively on entry-level plans, the same source notes LLM Pulse has limited native search-volume prompt discovery compared to competitors [79].

Questions to Verify Before Buying

Questions This Section Answers

  • What should a buyer confirm with LLM Pulse before signing a white-label agency contract?
  • Which LLM Pulse terms are undocumented and require a written quote?

The following items are unresolved in the supplied evidence and should be confirmed in writing before purchase.

Pricing and billing: the exact one-time and recurring price for Full Whitelabel and Embed Integration in USD; whether white-label fees are included in Enterprise pricing or billed separately for custom domain, embedding, API, projects, users, and support; whether the approximately 17% annual discount applies uniformly across all tiers; and how currency conversion is handled, including whether any fixed-rate annual agreement is available [80].

Capacity and limits: how many client projects, prompts, users, domains, API calls, and tracked models are included in the proposed package; the exact scaling path from Scale++ to Enterprise and the client count or prompt volume at which Enterprise pricing begins; and whether long-tail models are available as standalone add-ons or only bundled into Enterprise contracts [81].

Branding and access: whether Full Whitelabel removes all vendor branding from emails, help content, URLs, metadata, exports, and support interactions; whether agencies can create role-based client access with separate permissions, data isolation, and client-specific dashboards; and whether SSO is included in the white-label package and on which tier [80].

Technical and governance: API rate limits, export formats, historical-data limits, webhook limits, and data-retention rules; contractual uptime, support-response, onboarding, and incident-notification commitments; and available security documentation, data-processing agreement, subprocessors list, and compliance certifications [80].

Methodology and references: how AI visibility scores, citations, recommendations, sentiment, and competitor comparisons are calculated and validated; what happens to client data, dashboards, domains, and historical reports if the subscription is cancelled; and whether LLM Pulse can provide references from three to five agencies actively reselling or white-labeling the platform [80].

Final AI Consensus Verdict

LLM Pulse is a good, but not fully verified, fit for US agencies seeking white-label AI visibility delivery. Four of seven platforms named it during ranking discovery, it finished first overall, and the fit ratings across platforms ranged from strong to uncertain, with three platforms rating it strong, three rating it good, and one rating it uncertain.

The strongest reason to consider it is that white-label delivery is a first-class product feature rather than a reseller workaround. Full White-Label provides a custom domain with no vendor branding, Embed Integration supports iframe deployment with JWT authentication for agencies that already run a client portal, and multi-client dashboards with unlimited seats are reported as standard on self-serve plans [85].

The main limitation is that the commercial and governance details buyers need most are the least documented. Full white-label pricing is quote-based, public pricing is in EUR with daily FX conversion, prompt and project limits conflict across sources, SOC 2 Type II is unverified and reported as unavailable, and most evidence is company-owned rather than independent. Buyers should obtain a written quote and verify capacity, commercial terms, data governance, service levels, and the precise feature set before purchase.

For a broader view of how this platform compares with the other options evaluated for the same buyer, see the White-Label AI Visibility Platforms for Agencies consensus index.

Agencies that want to compare this niche against the wider set of monitoring tools can browse the ai visibility llm monitoring category directory.

How This Review Was Produced

This review was produced from seven platform fit-research responses collected for the research date 2026-09-19. Each platform independently evaluated LLM Pulse against the same use case: a marketing or SEO agency that wants to offer AI visibility reporting under its own brand, with multi-client management, scalable prompt monitoring, competitor comparisons, citation and recommendation tracking, exportable reporting, and white-label or client-facing functionality.

Platform mentions in the ranking stage count only platforms that named LLM Pulse during ranking discovery. All seven platforms evaluated fit, but only four named the entity during ranking. Fit ratings were recorded per platform and are reported as supplied.

Citations are platform-reported evidence, not independently verified facts. Company-owned citations materially outnumber independent citations in this study, so company claims are labeled as company-reported rather than independently established. No personal testing, customer interviews, or independent verification was performed for this review.

Methodology Limitations

Several limitations affect how much weight this review can carry.

Platform-reported research dates differ from the authoritative run date. Six platforms used 2026-09-19, but DeepSeek used 2026-01-15, and its response ran without search enabled, making it the weakest evidence in the set [89]. Platform-reported dates are provenance metadata and do not independently prove freshness.

Pricing and plan details conflict across sources. Public sources disagree on plan names, tier thresholds, prompt limits, and currency equivalents, and the exact commercial name and price of the agency white-label package is not confirmed in any source. These conflicts are reported rather than resolved.

Company-owned sources dominate. Of the deduplicated citations, 22 are company-owned and 10 are independent, with none unclear. Company claims about setup timelines, scaling from one client to hundreds, and enterprise-grade security are company-reported and not independently verified.

The supplied URLs were collected from platform responses and were not independently validated by the writer stage. One platform noted that official-site retrieval failed during its normalization audit, so the verified domain key may not fully represent site content [89].

No source in this study shows a completed SOC 2 Type II attestation, and no source provides independently audited evidence of measurement accuracy, uptime, or client outcomes. AI-platform agreement on a product's suitability does not prove product quality.

Sources

Company-Owned Sources

Independent Sources

  • LLM Pulse: AI visibility with API, CLI and MCP server: https://citedindex.com/llm-pulse
  • LLM Pulse Review 2026: AI Visibility Tracker With MCP | TMB: https://thatmarketingbuddy.com/software/llm-pulse
  • LLM Pulse Review 2026: Pricing, Trial & Alternatives | Trakkr: https://trakkr.ai/reviews/llm-pulse-review
  • LLM Pulse Features: Why the Product Feels Broader Than Most | Trakkr: https://trakkr.ai/reviews/llm-pulse-review/features
  • LLM Pulse Limitations and Weak Spots: https://trakkr.ai/reviews/llm-pulse-review/limitations
  • LLM Pulse Pricing 2026: All Plans (€49 to €1,086), Trial & Alternatives | Trakkr: https://trakkr.ai/reviews/llm-pulse-review/pricing
  • Who LLM Pulse Is Best For | Trakkr: https://trakkr.ai/reviews/llm-pulse-review/who-its-for
  • Additional AI research evidence89 records
    1. AI research evidence record deepseek:c1
    2. AI research evidence record openai:c1
    3. AI research evidence record anthropic:citation_2
    4. AI research evidence record anthropic:citation_3
    5. AI research evidence record google:1.3.1
    6. AI research evidence record openai:c2
    7. AI research evidence record google:1.1.4
    8. AI research evidence record anthropic:citation_1
    9. AI research evidence record anthropic:citation_2
    10. AI research evidence record grok:0
    11. AI research evidence record grok:1
    12. AI research evidence record kimi:llmpulse-agencies-1
    13. AI research evidence record perplexity:c2
    14. AI research evidence record openai:c1
    15. AI research evidence record openai:c3
    16. AI research evidence record anthropic:citation_8
    17. AI research evidence record anthropic:citation_19
    18. AI research evidence record anthropic:citation_20
    19. AI research evidence record anthropic:citation_10
    20. AI research evidence record anthropic:citation_11
    21. AI research evidence record anthropic:citation_12
    22. AI research evidence record anthropic:citation_13
    23. AI research evidence record google:1.3.3
    24. AI research evidence record anthropic:citation_17
    25. AI research evidence record anthropic:citation_18
    26. AI research evidence record openai:c2
    27. AI research evidence record anthropic:citation_5
    28. AI research evidence record grok:2
    29. AI research evidence record google:1.1.2
    30. AI research evidence record perplexity:c6
    31. AI research evidence record google:1.1.1
    32. AI research evidence record anthropic:citation_25
    33. AI research evidence record anthropic:citation_24
    34. AI research evidence record anthropic:citation_14
    35. AI research evidence record anthropic:citation_15
    36. AI research evidence record google:1.2.4
    37. AI research evidence record kimi:llmpulse-agencies-1
    38. AI research evidence record kimi:llmpulse-agencies-1
    39. AI research evidence record google:1.3.2
    40. AI research evidence record anthropic:citation_1
    41. AI research evidence record anthropic:citation_19
    42. AI research evidence record anthropic:citation_20
    43. AI research evidence record google:1.1.2
    44. AI research evidence record anthropic:citation_11
    45. AI research evidence record anthropic:citation_12
    46. AI research evidence record anthropic:citation_13
    47. AI research evidence record openai:c1
    48. AI research evidence record google:1.3.5
    49. AI research evidence record anthropic:citation_16
    50. AI research evidence record openai:c2
    51. AI research evidence record anthropic:citation_5
    52. AI research evidence record google:1.1.2
    53. AI research evidence record grok:2
    54. AI research evidence record google:1.1.4
    55. AI research evidence record perplexity:c1
    56. AI research evidence record anthropic:citation_21
    57. AI research evidence record anthropic:citation_23
    58. AI research evidence record openai:c1
    59. AI research evidence record anthropic:citation_11
    60. AI research evidence record anthropic:citation_12
    61. AI research evidence record kimi:llmpulse-agencies-1
    62. AI research evidence record anthropic:citation_21
    63. AI research evidence record anthropic:citation_25
    64. AI research evidence record anthropic:citation_24
    65. AI research evidence record openai:c2
    66. AI research evidence record anthropic:citation_5
    67. AI research evidence record anthropic:citation_7
    68. AI research evidence record anthropic:citation_14
    69. AI research evidence record anthropic:citation_15
    70. AI research evidence record anthropic:citation_27
    71. AI research evidence record openai:c2
    72. AI research evidence record anthropic:citation_25
    73. AI research evidence record anthropic:citation_24
    74. AI research evidence record anthropic:citation_5
    75. AI research evidence record kimi:ayzeo-agencies-2
    76. AI research evidence record kimi:agenticlens-agencies-4
    77. AI research evidence record kimi:georion-agencies-5
    78. AI research evidence record kimi:routeless-agencies-3
    79. AI research evidence record google:1.2.4
    80. AI research evidence record openai:c2
    81. AI research evidence record anthropic:citation_5
    82. AI research evidence record anthropic:citation_14
    83. AI research evidence record perplexity:c1
    84. AI research evidence record anthropic:citation_25
    85. AI research evidence record openai:c1
    86. AI research evidence record anthropic:citation_1
    87. AI research evidence record anthropic:citation_2
    88. AI research evidence record kimi:llmpulse-agencies-1
    89. AI research evidence record deepseek:c1

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
Source records
32
Ranking mentions
4 of 7
Platform share
57%
Final consensus rank
#1

Research trail and source mix

Configured platforms

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

Source mix

10 independent · 22 company-owned

Evidence support

26 direct · 6 partial

Important limitation

Use the run research_date as the study date. Platform-reported dates are provenance metadata and do not independently prove freshness.

Source snapshot SHA-256 a66213beb579deea04d3ceb71f89cde2aff1d63f43f9d55dea9de4f856070bf5