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AI Consensus Fit Review

Conductor AI Visibility Platform Fit Review for SaaS Companies

Conductor is a strong fit for enterprise and upper-midmarket SaaS companies that need AI visibility tracking bundled with SEO, content, and technical monitoring.

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

Answer Capsule

Conductor is a strong fit for enterprise and upper-midmarket SaaS companies that need AI visibility tracking bundled with SEO, content, and technical monitoring. Two of seven platforms named Conductor during the ranking stage (deepseek and kimi), giving it a 28.6% share of included platform responses, an average listed rank of 4.5, and a best listed rank of 2. The strongest reason to consider it is its breadth: prompt monitoring, competitive benchmarking, citation analysis, historical trends, and connected SEO/content execution in one platform [1]. The main limitation is opacity — Conductor publishes no dollar price for the relevant module, and independent sources disagree on citation-tracking depth and total cost [3].

Research Snapshot

FieldValue
Platform mentions in ranking stage2 of 7
Share of included platform responses28.6%
Average listed rank4.5
Best listed rank2
Relevant product/model/planConductor AI Search Performance within Conductor Intelligence, with related AEO capabilities
Overall use-case fitStrong for enterprise/upper-midmarket; mixed for smaller or price-sensitive SaaS buyers
Research date2026-09-19

Why Conductor Qualified for This Study

Questions This Section Answers

  • Why did Conductor qualify for this AI visibility platform study for SaaS companies?
  • How many AI platforms named Conductor during the ranking stage for SaaS AI visibility?

Conductor qualified because it is one of the few platforms that markets AI visibility, prompt monitoring, competitive benchmarking, and citation analysis as an integrated product rather than a standalone prompt tracker. Two of the seven included platforms — deepseek and kimi — named Conductor during the ranking stage, producing a 28.6% share of included platform responses, an average listed rank of 4.5, and a best listed rank of 2 (deepseek ranked it 7th; kimi ranked it 2nd).

The remaining five platforms (openai, anthropic, google, grok, perplexity) evaluated Conductor's fit but did not name it in their ranking lists. That distinction matters: fit evaluation and ranking discovery are separate signals, and Conductor's qualification rests on a minority of the panel.

Conductor's relevant offering is Conductor Intelligence with the AI Search Performance module, plus related Answer Engine Optimization capabilities [5]. The platform is positioned as an enterprise AEO/SEO operating layer rather than a narrow AI-visibility point tool [8].

The Product, Model, Plan, or Service Most Relevant to AI Visibility Platforms for SaaS Companies

Questions This Section Answers

  • Which Conductor product or plan is most relevant for a SaaS company tracking AI visibility?
  • Does Conductor Intelligence include AI Search Performance and prompt monitoring for SaaS buyer-intent queries?

The relevant product is Conductor Intelligence with the AI Search Performance module, supported by Conductor Monitoring for technical AEO signals and Conductor Creator for content execution [10]. Conductor describes its AI capabilities as integrated across Intelligence, Creator, and Monitoring, covering AI visibility, content optimization, and AI crawler activity [13].

For SaaS buyer-intent tracking specifically, Conductor maps prompts to a full customer lifecycle — Education, Recommendations, Comparison, Pricing, Brand Navigation, and Purchase — and generates prompts using the site's own data [14]. The platform generates up to 100 prompts per topic and allows manual custom prompt definition [15]. Persona and intent customization, topic-level reporting, and tracking by brand, region, and domain are documented [16].

Engine coverage is reported across ChatGPT (including separate Auto and Search modes), Perplexity, Google AI Overviews, Google AI Mode, Copilot, Gemini, and Claude, though exact availability may depend on the purchased configuration [18]. One independent review claims a competitor covers more engines, but Conductor documentation confirms five major engines are tracked [19].

What the AI Platforms Agreed About

Questions This Section Answers

  • What do AI platforms agree Conductor does well for SaaS AI visibility tracking?
  • Is Conductor's prompt monitoring and competitive benchmarking considered strong across AI platform evaluations?

The clearest cross-platform agreement is that Conductor covers the core capabilities this use case requires: prompt monitoring, competitive benchmarking, citation analysis, and historical trends. OpenAI, Anthropic, Google, Grok, and Perplexity all assessed these as advantages [21].

Prompt customization is consistently documented. Conductor supports AI-generated prompt suggestions, editable prompts, persona and intent customization, and topic-level reporting [26]. Competitive benchmarking is also widely supported: topic-level market-share analysis, competitor visibility, mentions, and citations across AI and traditional search [28]. Conductor tracks up to 50 competitors simultaneously per one independent review [30].

Citation analysis is documented by the vendor as reporting cited domains, cited pages, citations, mentions, sentiment, and citation performance by engine [21]. Historical trends are supported through daily and weekly tracking, performance trends, and historical citation data [32].

Enterprise orientation is a shared observation. Multiple platforms describe Conductor as built for large marketing organizations with unified AI and traditional search data [34].

Where the AI Platforms Disagreed or Were Uncertain

Questions This Section Answers

  • Do AI platforms disagree about Conductor's citation tracking depth for SaaS AI visibility?
  • How much does Conductor actually cost per year for a SaaS company, and why do estimates vary so widely?

Citation-tracking depth is the sharpest conflict. Conductor's own documentation states the visibility module shows which sources the model cited [37]. An independent 2026 review claims Conductor "still can't track AI citations" comprehensively or connect earned media to LLM visibility [39]. Kimi's evaluation also flagged citation analysis as a limitation, noting Conductor's published capabilities focus on content performance and technical SEO citations rather than AI-specific citation intelligence [40]. This conflict is unresolved in the supplied evidence.

Pricing estimates conflict substantially. Reported annual ranges include $26,800–$500,000+ [42], a median of $48,950 [43], $60,000–$300,000+ [44], and $150,000+ for enterprise deployments [42]. Conductor's own pricing page describes usage-based positioning and plan allowances without publishing a dollar figure for the relevant module [45].

Fit ratings diverged across platforms: openai and grok rated Conductor "strong," anthropic, google, and perplexity rated it "good," deepseek rated it "mixed," and kimi rated it "uncertain." Deepseek's research date was 2026-01-15, eight months earlier than the other six platforms, which may explain some of its thinner findings.

Engine coverage is also contested. RivalSee claims broader engine coverage than Conductor, while Conductor documentation confirms tracking of ChatGPT, Perplexity, Google AI, Gemini, and Claude [47].

Use-Case-Specific Features and Capabilities

Questions This Section Answers

  • Does Conductor support category, comparison, alternatives, and product-recommendation prompt tracking for SaaS?
  • Can Conductor benchmark a SaaS company against named competitors in AI-generated answers?

Conductor's documented capabilities map closely to the five criteria in this use case. Prompt monitoring is supported through AI-generated prompt suggestions, editable prompts, persona and intent customization, and topic-level reporting [49]. Prompts can be tagged by funnel stage and persona, and the platform maps prompts to Education, Recommendation, Comparison, Pricing, Brand Navigation, and Purchase stages [51].

Competitive benchmarking is documented at topic level with market-share analysis, competitor visibility, mentions, and citations across AI and traditional search [52]. One independent review reports tracking of up to 50 competitors [54].

Citation analysis is vendor-documented as reporting cited domains, cited pages, citations, mentions, sentiment, and citation performance by engine, with citation-domain analysis against competitors [50]. Independent verification of this depth is limited and contested [56].

Historical trends are supported through daily and weekly tracking, performance trends, and historical citation data for previously tracked prompts, subject to data-collection availability [57].

Workflow and execution connect AI visibility findings to Conductor Creator for content optimization, while Conductor Intelligence unifies AI visibility, traditional search, competitive intelligence, and website performance data [50]. Conductor Monitoring surfaces 20+ AEO-specific signals alongside 100+ traditional SEO signals with 24/7 monitoring [59].

SaaS buyer-intent coverage is assessed as neutral rather than an advantage: topic and prompt analysis can be configured around personas and intent, but Conductor does not publicly document a guaranteed SaaS-specific prompt taxonomy or outcome benchmark [50].

Pricing, Fees, Contracts, and Ongoing Costs

Questions This Section Answers

  • How much does Conductor cost per year for a SaaS company, and is there a published price?
  • What contract terms, cancellation rules, and overage fees should a SaaS buyer confirm with Conductor before signing?

Conductor does not publish a clear dollar price for the relevant AI Search Performance or Conductor Intelligence configuration. The public pricing page describes usage-based positioning, plan allowances, tracked competitors, AI Search credits, and sales-led enterprise packaging [61]. The official pricing page states pricing is "built around the value you get, not the seats you buy" and describes a usage-based model (official:C2).

Independent estimates vary widely and should be treated as unverified. Reported ranges include $26,800–$500,000+ annually with a median of $48,950 [63], $60,000–$300,000+ [65], and $150,000+ for enterprise deployments [64]. One source reports first-year implementations costing 1.5x to 2x more than subscription [63].

Plan allowances are documented at a high level: the Essentials plan includes 0 AI credits, the Growth plan includes 2,500 AI credits per year, and the Enterprise plan includes 2,500+ AI credits [62]. One independent review notes the Growth tier's 2,500 annual credits average roughly 208 per month, which may restrict high-volume prompt tracking [66].

Contract length, renewal, cancellation, refund, service-level, data-retention, and overage terms for the relevant module are not publicly verified [61]. Buyers should assume commercial terms require confirmation in the order form or master services agreement. Potential incremental costs may arise from higher AI Search credit usage, additional tracked competitors, pages, websites, domains, or custom enterprise allowances [61].

Best Suited For

Questions This Section Answers

  • Is Conductor a good choice for an enterprise SaaS company tracking AI visibility across multiple engines?
  • Which SaaS teams get the most value from Conductor's unified AI visibility and SEO platform?

Conductor is best suited for enterprise or upper-midmarket SaaS companies with established SEO, content, analytics, and enterprise marketing teams [68]. Organizations needing AI visibility and traditional search data in one operating platform are a strong match [70].

Teams tracking multiple competitors, personas, intents, regions, domains, and AI engines over time are well aligned with the platform's documented capabilities [68]. SaaS companies treating AI search as a primary revenue channel and needing unified tracking, content generation, technical monitoring, and API infrastructure in one platform are also a fit [73].

Organizations already invested in Conductor's traditional SEO platform seeking to add AI visibility without platform switching are a natural fit [75]. Brands with dedicated SEO teams requiring 24/7 monitoring, real-time alerting, and cross-functional access benefit from unlimited user licenses [76].

Probably Not Best Suited For

Questions This Section Answers

  • Who should not choose Conductor for AI visibility tracking as a SaaS company?
  • Is Conductor overkill for a small SaaS team that only needs prompt monitoring?

Early-stage or budget-constrained SaaS companies requiring public monthly pricing and rapid self-serve deployment are not a good fit [77]. Buyers wanting only lightweight prompt monitoring without broader enterprise SEO, content, monitoring, or implementation capabilities will find the platform broader than necessary [77].

Mid-market SaaS companies and agencies needing only AI visibility tracking without enterprise SEO, content creation, or technical monitoring are better served elsewhere [78]. Startups and small SaaS teams with limited budgets seeking transparent, published pricing and self-serve onboarding should look at alternatives [80].

Teams seeking dedicated AI-first visibility tools focused solely on citation analysis and mention tracking, not traditional SEO rank tracking, may find Conductor's bundled approach adds complexity [82]. One independent review describes Conductor as built for 50+ person marketing departments [78].

When Another Option May Be Better

Questions This Section Answers

  • What is a better alternative to Conductor for a SaaS buyer who needs transparent monthly pricing?
  • When should a SaaS company choose a dedicated AI visibility tool instead of Conductor?

A specialist AI visibility platform may be better when the buyer prioritizes a narrowly scoped prompt tracker, faster deployment, or simpler pricing over integrated SEO and content workflows [83]. Dedicated tools like Profound, RivalSee, Trakkr, or xSeek offer focused AI visibility at lower cost ($29–$300/month) with transparent pricing [84].

Semrush (published pricing with AI Visibility Toolkit), Ahrefs (Brand Radar), or SE Ranking offer published tiers and faster deployment for mid-market or smaller SaaS teams prioritizing pricing transparency and self-serve onboarding [84]. Budgets under $50,000 annually and teams under 20 people may be better served by cost-effective alternatives like RivalSee, Trakkr, SORN AI, Quattr, or Gauge [84].

When the primary need is earned media and PR-driven AI citations, AuthorityTech connects earned media placements to LLM visibility, filling a known Conductor gap [88]. When the buyer needs pure content generation optimization for AI answers without a full SEO suite, Scrunch AI or BotRic focus on content-to-visibility workflows [84].

Questions to Verify Before Buying

Questions This Section Answers

  • What should a SaaS buyer confirm with Conductor before signing a contract for AI visibility tracking?
  • Can Conductor demonstrate a SaaS-specific pilot with real competitors and prompts before purchase?

Buyers should verify which exact AI engines and modes are included for U.S. SaaS tracking, including ChatGPT Auto versus Search, Google AI Overviews, Google AI Mode, Perplexity, Gemini, Claude, and Copilot [89]. Prompt, topic, competitor, domain, region, and refresh limits should be confirmed, along with overage prices [91].

Buyers should confirm whether they can create and edit exact category, comparison, alternatives, and product-recommendation prompts, and whether prompts can be tagged by funnel stage, persona, and intent [93]. Historical retention periods for mentions, citations, cited pages, sentiment, and competitor data should be verified [95].

Raw response access, citations, page URLs, timestamps, prompt metadata, and exports/API access should be confirmed [97]. Onboarding, implementation, training, support, SLA, security, data-retention, renewal, and cancellation terms should be reviewed [91].

Buyers should ask whether Conductor can demonstrate a SaaS-specific pilot using real competitors and prompt sets before contract signature, and what independent evidence supports accuracy, repeatability, and correlation between reported visibility and qualified pipeline or revenue [100].

Final AI Consensus Verdict

Conductor is a strong fit for enterprise or upper-midmarket SaaS companies that need a broad AI visibility operating system: prompt monitoring, competitive benchmarking, citation analysis, historical trends, and connected SEO/content execution [102]. Fit is mixed for smaller or price-sensitive SaaS buyers because relevant pricing, limits, retention, and measurement methodology are not transparently published and the platform may be broader than necessary [105].

Two of seven platforms named Conductor during the ranking stage, giving it a 28.6% share of included platform responses, an average listed rank of 4.5, and a best listed rank of 2. Fit ratings diverged: openai and grok rated it "strong," anthropic, google, and perplexity rated it "good," deepseek rated it "mixed," and kimi rated it "uncertain."

The strongest reason to consider Conductor is its breadth and integration of AI visibility with existing SEO, content, and technical monitoring workflows [107]. The main limitation is opacity: no published dollar price for the relevant module, conflicting independent cost estimates, and contested citation-tracking depth [109].

How This Review Was Produced

This review synthesizes fit-research responses from seven AI platforms (openai, anthropic, google, grok, perplexity, deepseek, and kimi) collected on 2026-09-19. Each platform evaluated Conductor against the same use case: AI Visibility Platforms for SaaS Companies tracking category, comparison, alternatives, and product-recommendation prompts. Two platforms named Conductor during the ranking stage; all seven evaluated its fit.

Platform responses were treated as platform-reported evidence, not independently verified facts. Company-owned sources (Conductor's own documentation and marketing pages) are distinguished from independent sources (third-party reviews, pricing databases, and journalism). Where platforms disagreed, the conflict is preserved rather than resolved.

Methodology Limitations

Several limitations apply. Conductor's public pages describe broad and sometimes changing engine coverage; the exact engines, modes, locations, prompt limits, and historical retention included in a specific plan are unclear [111]. Vendor comparison and leadership claims are company-reported and should not be treated as independent proof of superior accuracy or ROI [113].

Public pricing provides usage allowances and sales-led packaging but no verified dollar price for the relevant module [115]. Independent pricing estimates conflict substantially and are not consistent with official disclosure [117].

G2 evidence supports user-reported relevance but may include vendor product descriptions and review-summary automation; it does not independently verify technical measurement quality [120]. Public evidence is primarily vendor-controlled, with third-party evidence focused mainly on general product reviews rather than independent validation of AI visibility accuracy [120].

Platform-reported research dates differ from the authoritative run date: deepseek's research date was 2026-01-15, while the other six platforms reported 2026-09-19. Deepseek's research was conducted without search enabled, so its findings are model-reported rather than retrieved. The supplied URLs were collected from platform responses and were not independently validated by the writer stage. AI-engine interfaces and answer behavior change frequently; reported visibility is an observation of sampled responses, not a guarantee of future recommendations or traffic [122].

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Sources

Company-Owned Sources

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    122. AI research evidence record openai:c2
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  • Additional AI research evidence123 records
    1. AI research evidence record openai:c2
    2. AI research evidence record anthropic:citation-22
    3. AI research evidence record openai:c11
    4. AI research evidence record anthropic:citation-46
    5. AI research evidence record openai:c2
    6. AI research evidence record anthropic:citation-22
    7. AI research evidence record perplexity:c1
    8. AI research evidence record anthropic:citation-2
    9. AI research evidence record google:1.1.3
    10. AI research evidence record openai:c4
    11. AI research evidence record anthropic:citation-7
    12. AI research evidence record google:1.2.5
    13. AI research evidence record openai:c12
    14. AI research evidence record anthropic:citation-24
    15. AI research evidence record anthropic:citation-26
    16. AI research evidence record openai:c1
    17. AI research evidence record openai:c2
    18. AI research evidence record openai:c3
    19. AI research evidence record anthropic:citation-8
    20. AI research evidence record anthropic:citation-44
    21. AI research evidence record openai:c2
    22. AI research evidence record anthropic:citation-22
    23. AI research evidence record google:1.2.3
    24. AI research evidence record grok:1
    25. AI research evidence record perplexity:c4
    26. AI research evidence record openai:c1
    27. AI research evidence record anthropic:citation-23
    28. AI research evidence record openai:c4
    29. AI research evidence record openai:c5
    30. AI research evidence record anthropic:citation-8
    31. AI research evidence record openai:c6
    32. AI research evidence record openai:c7
    33. AI research evidence record openai:c8
    34. AI research evidence record anthropic:citation-45
    35. AI research evidence record google:1.3.8
    36. AI research evidence record perplexity:c11
    37. AI research evidence record anthropic:citation-22
    38. AI research evidence record openai:c5
    39. AI research evidence record anthropic:citation-46
    40. AI research evidence record kimi:viali-product
    41. AI research evidence record kimi:optiseo-dashboard
    42. AI research evidence record anthropic:citation-13
    43. AI research evidence record google:1.3.2
    44. AI research evidence record anthropic:citation-42
    45. AI research evidence record openai:c11
    46. AI research evidence record google:1.3.1
    47. AI research evidence record anthropic:citation-44
    48. AI research evidence record anthropic:citation-8
    49. AI research evidence record openai:c1
    50. AI research evidence record openai:c2
    51. AI research evidence record anthropic:citation-24
    52. AI research evidence record openai:c4
    53. AI research evidence record openai:c5
    54. AI research evidence record anthropic:citation-8
    55. AI research evidence record openai:c6
    56. AI research evidence record anthropic:citation-46
    57. AI research evidence record openai:c7
    58. AI research evidence record openai:c8
    59. AI research evidence record anthropic:citation-6
    60. AI research evidence record google:1.2.2
    61. AI research evidence record openai:c11
    62. AI research evidence record google:1.3.1
    63. AI research evidence record google:1.3.2
    64. AI research evidence record anthropic:citation-13
    65. AI research evidence record anthropic:citation-42
    66. AI research evidence record google:1.3.4
    67. AI research evidence record grok:11
    68. AI research evidence record openai:c2
    69. AI research evidence record anthropic:citation-45
    70. AI research evidence record openai:c4
    71. AI research evidence record google:1.2.5
    72. AI research evidence record anthropic:citation-8
    73. AI research evidence record anthropic:citation-16
    74. AI research evidence record google:1.1.4
    75. AI research evidence record anthropic:citation-39
    76. AI research evidence record anthropic:citation-7
    77. AI research evidence record openai:c10
    78. AI research evidence record anthropic:citation-45
    79. AI research evidence record anthropic:citation-39
    80. AI research evidence record anthropic:citation-44
    81. AI research evidence record kimi:optiseo-dashboard
    82. AI research evidence record anthropic:citation-38
    83. AI research evidence record openai:c10
    84. AI research evidence record anthropic:citation-44
    85. AI research evidence record anthropic:citation-45
    86. AI research evidence record kimi:se-visible
    87. AI research evidence record anthropic:citation-39
    88. AI research evidence record anthropic:citation-46
    89. AI research evidence record openai:c3
    90. AI research evidence record anthropic:citation-8
    91. AI research evidence record openai:c11
    92. AI research evidence record grok:11
    93. AI research evidence record anthropic:citation-24
    94. AI research evidence record anthropic:citation-26
    95. AI research evidence record openai:c7
    96. AI research evidence record anthropic:citation-22
    97. AI research evidence record openai:c6
    98. AI research evidence record anthropic:citation-16
    99. AI research evidence record anthropic:citation-44
    100. AI research evidence record openai:c9
    101. AI research evidence record anthropic:citation-46
    102. AI research evidence record openai:c2
    103. AI research evidence record anthropic:citation-22
    104. AI research evidence record google:1.2.3
    105. AI research evidence record openai:c10
    106. AI research evidence record anthropic:citation-45
    107. AI research evidence record openai:c4
    108. AI research evidence record anthropic:citation-6
    109. AI research evidence record openai:c11
    110. AI research evidence record anthropic:citation-46
    111. AI research evidence record openai:c3
    112. AI research evidence record anthropic:citation-8
    113. AI research evidence record anthropic:citation-1
    114. AI research evidence record anthropic:citation-2
    115. AI research evidence record openai:c11
    116. AI research evidence record google:1.3.1
    117. AI research evidence record anthropic:citation-13
    118. AI research evidence record anthropic:citation-42
    119. AI research evidence record google:1.3.2
    120. AI research evidence record openai:c9
    121. AI research evidence record anthropic:citation-46
    122. AI research evidence record openai:c2
    123. AI research evidence record anthropic:citation-22

Other Sources

  • Conductor Delivers Next-Generation AI Search Performance: https://finance.yahoo.com/sectors/technology/articles/conductor-delivers-next-generation-ai-130000828.html
  • Frase vs Conductor: AI Visibility That Fixes Content: https://www.frase.io/vs/conductor
  • Additional AI research evidence123 records
    1. AI research evidence record openai:c2
    2. AI research evidence record anthropic:citation-22
    3. AI research evidence record openai:c11
    4. AI research evidence record anthropic:citation-46
    5. AI research evidence record openai:c2
    6. AI research evidence record anthropic:citation-22
    7. AI research evidence record perplexity:c1
    8. AI research evidence record anthropic:citation-2
    9. AI research evidence record google:1.1.3
    10. AI research evidence record openai:c4
    11. AI research evidence record anthropic:citation-7
    12. AI research evidence record google:1.2.5
    13. AI research evidence record openai:c12
    14. AI research evidence record anthropic:citation-24
    15. AI research evidence record anthropic:citation-26
    16. AI research evidence record openai:c1
    17. AI research evidence record openai:c2
    18. AI research evidence record openai:c3
    19. AI research evidence record anthropic:citation-8
    20. AI research evidence record anthropic:citation-44
    21. AI research evidence record openai:c2
    22. AI research evidence record anthropic:citation-22
    23. AI research evidence record google:1.2.3
    24. AI research evidence record grok:1
    25. AI research evidence record perplexity:c4
    26. AI research evidence record openai:c1
    27. AI research evidence record anthropic:citation-23
    28. AI research evidence record openai:c4
    29. AI research evidence record openai:c5
    30. AI research evidence record anthropic:citation-8
    31. AI research evidence record openai:c6
    32. AI research evidence record openai:c7
    33. AI research evidence record openai:c8
    34. AI research evidence record anthropic:citation-45
    35. AI research evidence record google:1.3.8
    36. AI research evidence record perplexity:c11
    37. AI research evidence record anthropic:citation-22
    38. AI research evidence record openai:c5
    39. AI research evidence record anthropic:citation-46
    40. AI research evidence record kimi:viali-product
    41. AI research evidence record kimi:optiseo-dashboard
    42. AI research evidence record anthropic:citation-13
    43. AI research evidence record google:1.3.2
    44. AI research evidence record anthropic:citation-42
    45. AI research evidence record openai:c11
    46. AI research evidence record google:1.3.1
    47. AI research evidence record anthropic:citation-44
    48. AI research evidence record anthropic:citation-8
    49. AI research evidence record openai:c1
    50. AI research evidence record openai:c2
    51. AI research evidence record anthropic:citation-24
    52. AI research evidence record openai:c4
    53. AI research evidence record openai:c5
    54. AI research evidence record anthropic:citation-8
    55. AI research evidence record openai:c6
    56. AI research evidence record anthropic:citation-46
    57. AI research evidence record openai:c7
    58. AI research evidence record openai:c8
    59. AI research evidence record anthropic:citation-6
    60. AI research evidence record google:1.2.2
    61. AI research evidence record openai:c11
    62. AI research evidence record google:1.3.1
    63. AI research evidence record google:1.3.2
    64. AI research evidence record anthropic:citation-13
    65. AI research evidence record anthropic:citation-42
    66. AI research evidence record google:1.3.4
    67. AI research evidence record grok:11
    68. AI research evidence record openai:c2
    69. AI research evidence record anthropic:citation-45
    70. AI research evidence record openai:c4
    71. AI research evidence record google:1.2.5
    72. AI research evidence record anthropic:citation-8
    73. AI research evidence record anthropic:citation-16
    74. AI research evidence record google:1.1.4
    75. AI research evidence record anthropic:citation-39
    76. AI research evidence record anthropic:citation-7
    77. AI research evidence record openai:c10
    78. AI research evidence record anthropic:citation-45
    79. AI research evidence record anthropic:citation-39
    80. AI research evidence record anthropic:citation-44
    81. AI research evidence record kimi:optiseo-dashboard
    82. AI research evidence record anthropic:citation-38
    83. AI research evidence record openai:c10
    84. AI research evidence record anthropic:citation-44
    85. AI research evidence record anthropic:citation-45
    86. AI research evidence record kimi:se-visible
    87. AI research evidence record anthropic:citation-39
    88. AI research evidence record anthropic:citation-46
    89. AI research evidence record openai:c3
    90. AI research evidence record anthropic:citation-8
    91. AI research evidence record openai:c11
    92. AI research evidence record grok:11
    93. AI research evidence record anthropic:citation-24
    94. AI research evidence record anthropic:citation-26
    95. AI research evidence record openai:c7
    96. AI research evidence record anthropic:citation-22
    97. AI research evidence record openai:c6
    98. AI research evidence record anthropic:citation-16
    99. AI research evidence record anthropic:citation-44
    100. AI research evidence record openai:c9
    101. AI research evidence record anthropic:citation-46
    102. AI research evidence record openai:c2
    103. AI research evidence record anthropic:citation-22
    104. AI research evidence record google:1.2.3
    105. AI research evidence record openai:c10
    106. AI research evidence record anthropic:citation-45
    107. AI research evidence record openai:c4
    108. AI research evidence record anthropic:citation-6
    109. AI research evidence record openai:c11
    110. AI research evidence record anthropic:citation-46
    111. AI research evidence record openai:c3
    112. AI research evidence record anthropic:citation-8
    113. AI research evidence record anthropic:citation-1
    114. AI research evidence record anthropic:citation-2
    115. AI research evidence record openai:c11
    116. AI research evidence record google:1.3.1
    117. AI research evidence record anthropic:citation-13
    118. AI research evidence record anthropic:citation-42
    119. AI research evidence record google:1.3.2
    120. AI research evidence record openai:c9
    121. AI research evidence record anthropic:citation-46
    122. AI research evidence record openai:c2
    123. AI research evidence record anthropic:citation-22

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
58
Ranking mentions
2 of 7
Platform share
29%
Final consensus rank
#7

Research trail and source mix

Configured platforms

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

Source mix

29 independent · 27 company-owned · 2 unclear

Evidence support

50 direct · 8 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 73177f14594e86964aedf0ad9db94cc34fdb580fd124768ed80cc6cc91e580af