One marketing need. Multiple leading AI platforms. One transparent consensus. How it works
AI MarketingConsensus Index

AI Consensus Fit Review

BeCited AI Search Agency Fit Review for AI Visibility Audits

BeCited is a good fit for buyers who want a human-reviewed, point-in-time AI visibility audit across four engines, but it is a weaker fit for agencies that need continuous dashboards, API access, or multi-client scale.

Research: 2026-09-187 usable platform responsesRead the methodology ↗

Answer Capsule

BeCited is a good fit for buyers who want a human-reviewed, point-in-time AI visibility audit across four engines, but it is a weaker fit for agencies that need continuous dashboards, API access, or multi-client scale. Two of seven platforms named BeCited during the ranking stage — DeepSeek (rank 5) and Kimi (rank 4) — giving it a 28.6% share of included platform responses and an average listed rank of 4.5. The strongest reason to consider it is the Full Audit: 100–300 buying-intent prompts across ChatGPT, Claude, Gemini, and Perplexity with source mapping, competitor comparison, and a 90-day action plan. The main limitation is that public materials do not verify agency-scale tooling, and pricing and contract terms conflict across sources.

Research Snapshot

FieldValue
Platform mentions in ranking stage2 of 7 platforms
Share of included platform responses28.6%
Average listed rank4.5
Best listed rank4 (Kimi)
Relevant product/model/planFull Audit ($2,000 one-time); Snapshot ($199 one-time); Quarterly ($1,500 per quarter)
Overall use-case fitGood for a one-time or quarterly human-reviewed audit; not verified for agency-scale operations
Research date2026-09-18

Why BeCited Qualified for This Study

Questions This Section Answers

  • Is BeCited a good choice for AI Search Agencies for AI Visibility Audits?
  • How many AI platforms named BeCited in the ranking stage for AI visibility audits?

BeCited qualified because two of the seven included platforms named it during ranking discovery for AI visibility audits, clearing the study's two-mention threshold. DeepSeek listed it at rank 5 and Kimi at rank 4, producing an average listed rank of 4.5 and a best listed rank of 4 [1].

Qualification is a threshold result, not a quality signal. Five of the seven platforms — OpenAI, Anthropic, Google, Grok, and Perplexity — evaluated BeCited's fit for this use case but did not name it in their ranking lists. Their fit ratings still count as evidence: Google rated the fit "strong," while OpenAI, Anthropic, Grok, Kimi, and Perplexity rated it "good," and DeepSeek rated it "uncertain" [3].

The entity is a company, not a platform: BeCited sells an analyst-led audit service rather than a self-service monitoring tool. That distinction matters for an agency buyer, because the deliverables are reports and strategy sessions rather than a persistent workspace [3].

The Product, Model, Plan, or Service Most Relevant to AI Search Agencies for AI Visibility Audits

Questions This Section Answers

  • Which BeCited plan is the best fit for an agency that needs a full multi-engine AI visibility audit?
  • Does the BeCited Snapshot at $199 cover enough engines for an agency audit deliverable?

The Full Audit is the plan most relevant to this use case. It is described as a $2,000 one-time engagement covering 100–300 buying-intent prompts across four engines — ChatGPT, Claude, Gemini, and Perplexity — with a visibility score, engine-by-engine breakdown, source map, prompt gap analysis, persona analysis, a prioritized 90-day action plan, and a strategy session [9].

The Snapshot is the entry tier at $199 one-time. It is narrow by design: ten buying-intent prompts on Perplexity only, a single-engine score, top three priority moves, a site-readiness grade, and a single-page HTML deliverable, with the $199 credited toward a Full Audit if purchased within 30 days [9]. For an agency producing a client-facing multi-engine audit, the Snapshot does not cover enough engines on its own.

The Quarterly plan is listed at $1,500 per quarter and is described as a re-run of the Full Audit with a delta report, trend tracking, an updated action plan, and a strategy call [9]. Google's response describes it as $1,500 per quarter, or $6,000 annually for four consecutive tracking reports [18].

A separate implementation service, Relevance Engineering, is scoped at $8,000–$25,000 per engagement on a flat-fee basis, typically spanning four to eight weeks [19]. That is a distinct offering from the audit itself.

What the AI Platforms Agreed About

Questions This Section Answers

  • What do AI platforms agree BeCited actually delivers in an AI visibility audit?
  • Is BeCited's audit based on human review rather than automated scanning?

Platforms broadly agreed on the shape of the offer, though the agreement rests heavily on BeCited's own published pages rather than independent testing.

Four-engine coverage was the most consistent finding. OpenAI, Anthropic, Grok, Perplexity, Kimi, and Google all describe the Full Audit as covering ChatGPT, Claude, Gemini, and Perplexity [20]. No platform claimed broader coverage for the Full Audit.

Human review was also widely reported. Anthropic states that every audit is performed personally by founder Owen Kurth with manual review of quotes [26]. Google describes the same single-analyst model and notes that BeCited positions itself away from selling dashboard-only SaaS tools [29]. OpenAI describes manual answer review, brand-variant matching, and manual competitor narrowing [30].

Deliverable content drew consistent descriptions: a visibility score, engine-level breakdown, source map, gap analysis, and a prioritized 90-day action plan [31].

Citation and source mapping was reported as a core output. Perplexity states the audit maps which sources AI engines cite and which competitor sources appear [33]. Google describes source tiering based on category-specific AI trust rather than conventional domain authority [34].

Where the AI Platforms Disagreed or Were Uncertain

Questions This Section Answers

  • Do AI platforms disagree about BeCited's pricing and plan names for AI visibility audits?
  • Is BeCited's audit methodology independently verified by any third party?

Pricing and plan naming conflict across sources, and the conflict is material for budgeting.

The ranking-stage input describes a "Full Audit ($2,000 one-time)" and a "Starter Audit ($199)," while BeCited's public pages call the $199 product "Snapshot" and list Quarterly at $1,500 per quarter [35]. Anthropic's response notes that a $199 Starter Audit was referenced in its brief but not confirmed on the current website or in search results [36]. DeepSeek labels all three tiers buyer-supplied and unverified [37]. Grok and Kimi both list Snapshot at $199, Full Audit at $2,000, and Quarterly at $1,500 per quarter [38].

Site-readiness counts are inconsistent. OpenAI reports that BeCited pages refer to 19 site-readiness signals while a case-study page describes 15 checks [40]. Kimi reports a Snapshot site-readiness grade of 14 signals and a Full Audit with 15 checks [39]. Google reports a free site scan testing 9 of 19 signals [41]. These counts cannot be reconciled from the supplied evidence.

Methodology validation is company-published only. OpenAI reports that BeCited's methodology describes a reported 72% inter-rater agreement, and Google reports a Cohen's kappa of 0.722 with 95% confidence intervals [42]. Both are company-published claims. OpenAI states explicitly that independent evidence confirming audit accuracy, customer outcomes, repeatability, or the reported agreement figure was not identified in the reviewed sources [42]. Perplexity, Kimi, and Grok likewise report finding no independent third-party review or benchmark [44].

DeepSeek's assessment is the outlier on confidence. Its research ran without search enabled, and it rated fit "uncertain," stating that no independent confirmation of platform coverage, prompt tracking, mention measurement, citation tracking, competitor benchmarking, historical reporting, dashboards, pricing, or contract terms was retrievable [37]. That is a research-capability limitation, not a finding that the capabilities are absent.

Use-Case-Specific Features and Capabilities

Questions This Section Answers

  • Does BeCited provide citation tracking and source mapping for an AI visibility audit?
  • Can BeCited benchmark an agency's client against competitors in AI search results?

Multi-platform coverage is the first criterion, and BeCited's Full Audit covers four engines. OpenAI notes this is meaningful coverage for core AI search but not comprehensive coverage of every generative-answer or recommendation platform [45]. Kimi frames the same four-engine scope as neutral, falling short of six-plus platform coverage offered by competitors such as SE Visible, Indexable AI, and Appearly [46]. Anthropic notes the absence of Google AI Overviews, Google AI Mode, Microsoft Copilot, and Bing Chat from the covered set [50].

Prompt tracking and recommendation measurement are reported as advantages. BeCited states it measures whether the buyer is mentioned or recommended for real buying-intent prompts, with prompt-by-prompt gap analysis and manual review of answers [45]. Perplexity reports that the audit measures visibility rate, recommendation strength, and share of voice across the four engines [52]. Kimi quotes the positioning directly: "We don't measure rankings. We measure recommendations" [46].

Citation tracking and source mapping are reported as advantages by OpenAI, Perplexity, and Google [51]. Kimi rates this neutral, noting source mapping exists but that no automated citation decay monitoring or URL-level citation indexing is disclosed [46].

Competitor benchmarking is reported as an advantage by OpenAI, Perplexity, and Google, which describe scoring against real competitors and outputting category rank and recommendation rates [45]. Anthropic rates it neutral because the competitor set is determined during discovery rather than standardized, making fixed-scope comparison difficult [56]. Kimi notes no explicit side-by-side visibility score or share-of-voice percentage is stated [46].

Historical trend reporting splits by plan. The Quarterly plan includes a re-run, delta report, and trend tracking [45]. The one-time Full Audit does not itself provide ongoing historical reporting [45].

Executive-ready dashboards are the clearest limitation. OpenAI states the public offer describes a report and strategy session rather than a persistent executive dashboard [45]. Grok, Kimi, and Google report no dashboards [58]. Perplexity rates this unclear, noting dimensional breakdowns and a score are mentioned but a clearly labeled executive dashboard is not verified [57].

Agency suitability is unverified. OpenAI states that public materials do not verify multi-client workspaces, white labeling, API access, bulk prompt management, or resale rights [45]. Kimi lists no API access, white-label capability, or self-service dashboard for agency client management [46]. Anthropic notes the founder-led model limits concurrent audit capacity [60].

Pricing, Fees, Contracts, and Ongoing Costs

Questions This Section Answers

  • How much does the BeCited Full Audit cost, and what does the $199 Snapshot include?
  • What are BeCited's cancellation, refund, and report-reuse terms for the Quarterly plan?

Published pricing centers on three tiers, but the sources do not fully agree and the terms leave gaps.

Snapshot is $199 one-time with 48-hour delivery, ten Perplexity prompts, a single-engine score, top three priority moves, a site-readiness grade, and a single-page HTML deliverable, creditable toward a Full Audit within 30 days [61]. Full Audit is $2,000 one-time with one-week delivery and 100–300 prompts across four engines [65]. Quarterly is $1,500 per quarter [61].

Anthropic's response states that the Quarterly Subscription's price, billing cycle, contract term, and additional features are not published, and that the $199 Starter Audit tier was not confirmed on the current website [65]. DeepSeek labels all three amounts buyer-supplied and unverified [67]. Treat the public website as the current visible reference and confirm in writing.

On fees, OpenAI reports no separate implementation, platform, API, travel, or overage fees are publicly stated, and advises verifying whether custom work or additional prompt volume is separately priced [61]. Perplexity reports no setup fee was verified and that a strategy session is included with the Full Audit [68]. Grok lists no additional fees [64].

On contracts, BeCited's terms state that payment is due upon engagement unless otherwise agreed, and that dissatisfied customers should contact BeCited within 14 days of delivery to discuss resolution — but no standard refund policy is stated [69]. The terms also state that audit reports are licensed for internal business use and may not be resold, redistributed, or publicly shared without written permission [69]. That licensing restriction is directly relevant to an agency that wants to hand reports to clients or reuse them across accounts.

Quarterly renewal, cancellation, pause, and prorated-refund rules are not clearly stated in the public terms [69]. Grok reports one-time payments via Stripe with no recurring contracts detailed for the Full Audit [64]. Google reports no long-term contracts or recurring retainers required for standard audits, with Quarterly Tracking billed per quarter [66].

For market context, an independent pricing guide states that professional AI visibility audits typically cost $8,000–$25,000 one-time, varying by competitive set size and prompt basket depth, and that core audit work involves running 50–500 high-intent prompts through each engine [70]. That places the $2,000 Full Audit at the lower end of the published range, though the comparison is not like-for-like across vendors.

Best Suited For

Questions This Section Answers

  • Who gets the most value from a BeCited Full Audit for AI visibility work?
  • Is BeCited worth it for a buyer who wants a one-time AI visibility baseline before a longer GEO program?

BeCited is best suited to buyers who want a human-reviewed baseline rather than a monitoring subscription.

The strongest fit is a company that needs a point-in-time diagnostic before committing to a longer GEO program. An independent review states that an AI visibility audit is the fastest way to quantify gaps before committing budget to ongoing programs [72]. The Full Audit's one-week turnaround and $2,000 price support that use [73].

Second, buyers who prioritize recommendation and mention measurement over conventional search rankings. BeCited measures whether a brand is mentioned or recommended for buying-intent prompts rather than tracking rankings [74].

Third, teams that need an executive-readable output: a 0–100 visibility score, engine-level breakdown, source map, prompt gap analysis, and a prioritized 90-day action plan, plus a strategy session with the analyst [74].

Fourth, buyers who want periodic rather than continuous tracking. The Quarterly plan's re-run and delta report support trend measurement when quarterly reassessment is sufficient [74].

Fifth, buyers who value manual verification. Anthropic frames the founder-led, hand-read approach as reducing citation hallucination risk and ensuring actionable accuracy in the report [79]. That is a platform-reported characterization of the method, not an independently measured accuracy result.

Probably Not Best Suited For

Questions This Section Answers

  • Who should not choose BeCited for an AI Search Agency's AI visibility audit?
  • Is BeCited a poor fit for an agency that needs multi-client dashboards and API access?

BeCited is probably not the best choice for buyers whose core requirement is continuous, self-service, or multi-client tooling.

Organizations needing a self-service dashboard or continuous daily monitoring are a poor fit. OpenAI states the offer is less suitable for buyers requiring a continuously accessible dashboard [81]. Anthropic states BeCited is a snapshot audit, not a monitoring platform, and directs buyers needing weekly or daily tracking to dedicated SaaS platforms [82]. Kimi lists no continuous automated monitoring, no API access, no white-label capability, and no self-service dashboard for agency client management [84].

Agencies managing many clients that need documented API, workspace, export, or white-label capabilities are also a poor fit on current public evidence [81]. Anthropic notes the founder-led model limits concurrent audit capacity, making it unsuitable for agencies or multi-site enterprises needing simultaneous audits across dozens of domains [85].

Buyers needing coverage beyond the four listed engines should look elsewhere. OpenAI states broader AI search, answer, shopping, local, social, or recommendation coverage is not verified [81]. Anthropic notes the absence of Google AI Overviews, Google AI Mode, Microsoft Copilot, and Bing Chat, and cites an independent finding that only 12% of AI citations overlap across ChatGPT, Perplexity, Gemini, and Claude [86].

Buyers who need implementation, not just recommendations, should note that the 90-day action plan is a recommendation document. Anthropic states BeCited does not offer implementation services, content optimization, or schema deployment as part of the audit offering [88]. Relevance Engineering is a separately scoped engagement at $8,000–$25,000 [89].

Enterprises requiring standardized SLAs, data-processing terms, renewal rules, or documented implementation support are not clearly served by the published terms [90].

When Another Option May Be Better

Questions This Section Answers

  • What is a better alternative to BeCited for an agency that needs continuous AI visibility monitoring?
  • Which alternative covers more AI platforms than BeCited's four engines?

Another option may be better in four common situations, based on platform-reported comparisons.

When continuous real-time tracking is required. Anthropic names ZipTie, Allmond, and Athena HQ as offering daily or weekly monitoring and competitor benchmarking dashboards suitable for ongoing GEO programs [92]. Kimi names SE Visible, optiseo, and Appearly for continuous weekly or daily tracking [94].

When broader platform coverage is mandatory. Anthropic reports Veza Digital covers six platforms including Google AI Overviews and AI Mode at a $4,500 fixed price with 30-day tracking, and that Onely and ZipTie track seven platforms [98]. Kimi names SE Visible, Indexable AI, and Presenc AI for six-platform coverage including Google AI Overviews, AI Mode, Copilot, and Grok [94].

When white-label dashboards and API integration are required. Kimi names Appearly and optiseo [94].

When the buyer needs integrated audit-to-implementation work. Anthropic names Searchbloom, WebFX, and Coalition as combining audit findings with implementation services and tracking visibility lift from optimization efforts [92]. Note that these are platform-reported comparisons drawn from vendor and review pages, not head-to-head testing.

For buyers who want to compare the full field before deciding, the AI Search Agencies for AI Visibility Audits index collects the ranked options from this study.

Questions to Verify Before Buying

Questions This Section Answers

  • What should a buyer confirm with BeCited about prompt counts and engine coverage before paying?
  • What contract terms should an agency verify before buying the BeCited Quarterly plan?

The platforms surfaced a consistent verification list. Confirm these in writing before paying.

Scope and coverage: which exact models, account types, regions, personalization settings, and search dates are used for each engine; whether the Full Audit covers the platforms the agency needs; and whether the four engines can be customized by market or vertical [101].

Prompt volume: the exact prompt count delivered in a specific engagement and whether it can be customized, since the published range is 100–300 without a stated determinant [103].

Deliverable format: whether the Full Audit includes dashboard access and downloadable structured data or only an HTML/PDF-style report, and whether output is a client-ready dashboard, slide deck, or PDF [101].

Reuse rights: whether the agency can reuse or white-label reports for its own clients, and whether multi-client discounts exist — the terms restrict reports to internal business use without written permission [105].

Citation tracking detail: what the source map includes — URLs, domains, citation frequency, source influence, or all of these [101].

Competitor scope: how many competitors are included and whether the buyer can approve the prompt set before execution [101].

Quarterly terms: renewal, cancellation, pause, refund, and price-change rules, plus whether re-audits use the same prompt panel to track trends or re-curate prompts each quarter [105].

Site-readiness count: which signal count is contractually included — 15, 19, or another number [108].

Technical scope: whether the site-readiness audit tests AI crawler access such as robots.txt and bot permissions, or only general signals [103].

Evidence: whether BeCited can provide anonymized sample deliverables, references, or third-party reviews, and explain how score confidence intervals are calculated [110].

Final AI Consensus Verdict

BeCited is a good fit for AI Search Agencies for AI Visibility Audits when the deliverable is a human-reviewed, point-in-time or quarterly audit across ChatGPT, Claude, Gemini, and Perplexity. Six of seven platforms rated the fit good or strong; DeepSeek rated it uncertain, and its research ran without search enabled [112].

The case rests on the Full Audit's scope and output: 100–300 buying-intent prompts, source mapping, competitor comparison, persona analysis, a 0–100 score, and a prioritized 90-day plan with a strategy session, at $2,000 one-time [112].

The case against rests on what is not verified. No platform found independent confirmation of audit accuracy, customer outcomes, or the reported inter-rater agreement [121]. Agency-scale capabilities — multi-client workspaces, white labeling, API access, bulk prompt management, resale rights — are not verified in public materials [112]. Pricing and plan names conflict across sources, and site-readiness counts range from 14 to 19 depending on the page [122].

For an agency buyer, the practical read is this: BeCited fits as a diagnostic layer, not as an operating platform. Buyers who need continuous monitoring, broad platform coverage, or documented enterprise terms should compare against the monitoring and full-service options named in this study before committing. Buyers who want a rigorous, human-reviewed baseline with an actionable plan should verify the items above and treat the public website as the current pricing reference.

How This Review Was Produced

This review was produced from a seven-platform research run dated 2026-09-18. Each platform independently evaluated BeCited's fit for the use case "AI Search Agencies for AI Visibility Audits" and, where applicable, named the entity during ranking discovery. Two platforms named BeCited in the ranking stage; all seven supplied fit assessments, use-case findings, pricing notes, limitations, and verification questions.

The report preserves platform-reported claims as reported. Company-owned citations materially outnumber independent citations in the supplied evidence, so BeCited's own published claims are labeled as company-published rather than independently verified. Where platforms disagreed, the disagreement is described rather than resolved.

Methodology Limitations

Several limitations constrain this review.

Platform-reported research dates differ from the authoritative run date. DeepSeek's response carries a research date of 2026-02-14, while the run date is 2026-09-18. Platform-reported dates are provenance metadata and do not independently prove freshness [124].

DeepSeek's research ran without search enabled, so its "uncertain" rating reflects a research-capability limitation rather than a finding that capabilities are absent [124].

Company-owned sources dominate the evidence base. BeCited's own pages supply most of the detail on coverage, methodology, deliverables, and pricing. Independent sources in the set are largely vendor or review pages comparing other providers, not evaluations of BeCited [125].

Pricing and plan naming conflict across sources and were not resolved. The $199 tier is called "Snapshot" on the public site and "Starter Audit" in the ranking-stage input, and site-readiness counts range from 14 to 19 across pages [134].

The supplied URLs were collected from platform responses and were not independently validated by the writer stage. Citations are platform-reported evidence, not independently verified facts. No platform reported personal testing, customer experience, or independent verification of BeCited's results.

Platform agreement on a finding does not establish product quality. It indicates that multiple models described the same publicly available information.

Explore more ai search geo agencies guidance in the category directory.

Sources

Company-Owned Sources

  • BeCited — AI Search Visibility Audit: https://becited.io/
  • About BeCited — One analyst, calibrated method, real audits: https://becited.io/about/
  • GEO Audit — Measure where you stand in AI search - BeCited: https://becited.io/audit/
  • Generative Engine Optimization: Growth Strategies and Metrics: https://becited.io/blog/geo-growth-strategies-and-metrics
  • Case Studies — BeCited: https://becited.io/case-studies
  • Methodology — How BeCited keeps the audit honest: https://becited.io/methodology
  • Relevance Engineering — Move the needle, not the dashboard | BeCited: https://becited.io/relevance-engineering/
  • Services — Audits, tracking, and relevance engineering | BeCited: https://becited.io/services
  • Terms of Service — BeCited: https://becited.io/terms
  • Official pricing and terms source: https://becited.io#pricing
  • Additional AI research evidence136 records
    1. AI research evidence record deepseek:c1
    2. AI research evidence record kimi:c1
    3. AI research evidence record openai:becited_home
    4. AI research evidence record anthropic:4-2
    5. AI research evidence record google:1.2.3
    6. AI research evidence record grok:0
    7. AI research evidence record perplexity:c1
    8. AI research evidence record google:1.4.4
    9. AI research evidence record openai:becited_home
    10. AI research evidence record anthropic:4-2
    11. AI research evidence record anthropic:4-17
    12. AI research evidence record anthropic:4-20
    13. AI research evidence record anthropic:4-27
    14. AI research evidence record perplexity:c2
    15. AI research evidence record kimi:c1
    16. AI research evidence record perplexity:c1
    17. AI research evidence record grok:0
    18. AI research evidence record google:1.2.3
    19. AI research evidence record google:1.3.9
    20. AI research evidence record openai:becited_home
    21. AI research evidence record anthropic:4-2
    22. AI research evidence record grok:0
    23. AI research evidence record perplexity:c2
    24. AI research evidence record kimi:c1
    25. AI research evidence record google:1.2.3
    26. AI research evidence record anthropic:4-3
    27. AI research evidence record anthropic:28-3
    28. AI research evidence record anthropic:28-40
    29. AI research evidence record google:1.4.4
    30. AI research evidence record openai:becited_methodology
    31. AI research evidence record anthropic:4-17
    32. AI research evidence record anthropic:4-20
    33. AI research evidence record perplexity:c4
    34. AI research evidence record google:1.4.3
    35. AI research evidence record openai:becited_home
    36. AI research evidence record anthropic:4-4
    37. AI research evidence record deepseek:c1
    38. AI research evidence record grok:0
    39. AI research evidence record kimi:c1
    40. AI research evidence record openai:becited_case_studies
    41. AI research evidence record google:1.3.7
    42. AI research evidence record openai:becited_methodology
    43. AI research evidence record google:1.3.6
    44. AI research evidence record perplexity:c1
    45. AI research evidence record openai:becited_home
    46. AI research evidence record kimi:c1
    47. AI research evidence record kimi:c4
    48. AI research evidence record kimi:c5
    49. AI research evidence record kimi:c6
    50. AI research evidence record anthropic:4-2
    51. AI research evidence record openai:becited_methodology
    52. AI research evidence record perplexity:c3
    53. AI research evidence record perplexity:c4
    54. AI research evidence record google:1.4.3
    55. AI research evidence record google:1.2.7
    56. AI research evidence record anthropic:4-17
    57. AI research evidence record perplexity:c2
    58. AI research evidence record grok:0
    59. AI research evidence record google:1.4.4
    60. AI research evidence record anthropic:4-3
    61. AI research evidence record openai:becited_home
    62. AI research evidence record perplexity:c1
    63. AI research evidence record kimi:c1
    64. AI research evidence record grok:0
    65. AI research evidence record anthropic:4-4
    66. AI research evidence record google:1.2.3
    67. AI research evidence record deepseek:c1
    68. AI research evidence record perplexity:c2
    69. AI research evidence record openai:becited_terms
    70. AI research evidence record anthropic:5-2
    71. AI research evidence record anthropic:5-10
    72. AI research evidence record anthropic:8-5
    73. AI research evidence record anthropic:4-4
    74. AI research evidence record openai:becited_home
    75. AI research evidence record kimi:c1
    76. AI research evidence record anthropic:4-17
    77. AI research evidence record anthropic:4-27
    78. AI research evidence record perplexity:c2
    79. AI research evidence record anthropic:4-3
    80. AI research evidence record anthropic:28-40
    81. AI research evidence record openai:becited_home
    82. AI research evidence record anthropic:13-7
    83. AI research evidence record anthropic:22-4
    84. AI research evidence record kimi:c1
    85. AI research evidence record anthropic:4-3
    86. AI research evidence record anthropic:4-2
    87. AI research evidence record anthropic:23-1
    88. AI research evidence record anthropic:4-17
    89. AI research evidence record google:1.3.9
    90. AI research evidence record openai:becited_terms
    91. AI research evidence record perplexity:c1
    92. AI research evidence record anthropic:13-7
    93. AI research evidence record anthropic:22-4
    94. AI research evidence record kimi:c1
    95. AI research evidence record kimi:c2
    96. AI research evidence record kimi:c4
    97. AI research evidence record kimi:c6
    98. AI research evidence record anthropic:7-6
    99. AI research evidence record kimi:c3
    100. AI research evidence record kimi:c5
    101. AI research evidence record openai:becited_home
    102. AI research evidence record perplexity:c2
    103. AI research evidence record kimi:c1
    104. AI research evidence record grok:0
    105. AI research evidence record openai:becited_terms
    106. AI research evidence record anthropic:4-17
    107. AI research evidence record anthropic:4-4
    108. AI research evidence record openai:becited_case_studies
    109. AI research evidence record google:1.3.7
    110. AI research evidence record openai:becited_methodology
    111. AI research evidence record deepseek:c1
    112. AI research evidence record openai:becited_home
    113. AI research evidence record anthropic:4-2
    114. AI research evidence record google:1.2.3
    115. AI research evidence record grok:0
    116. AI research evidence record kimi:c1
    117. AI research evidence record perplexity:c1
    118. AI research evidence record deepseek:c1
    119. AI research evidence record anthropic:4-17
    120. AI research evidence record perplexity:c2
    121. AI research evidence record openai:becited_methodology
    122. AI research evidence record openai:becited_case_studies
    123. AI research evidence record google:1.3.7
    124. AI research evidence record deepseek:c1
    125. AI research evidence record openai:becited_home
    126. AI research evidence record anthropic:5-2
    127. AI research evidence record kimi:c2
    128. AI research evidence record kimi:c3
    129. AI research evidence record kimi:c4
    130. AI research evidence record kimi:c5
    131. AI research evidence record kimi:c6
    132. AI research evidence record anthropic:13-7
    133. AI research evidence record anthropic:22-4
    134. AI research evidence record openai:becited_case_studies
    135. AI research evidence record kimi:c1
    136. AI research evidence record google:1.3.7

Independent Sources

  • Appearly: AI Visibility Platform for Agencies: https://appearly.ai/
  • AI Search Audit | GEO Audit for Enterprise Brands — 14-Day Diagnostic | Indexable AI: https://indexableai.com/ai-search-audit/
  • Ai Visibility Dashboard - optiseo: https://optiseo.com/ai-visibility-dashboard/
  • Best AI Search Monitoring Tools (2026): Track Brand Visibility Across AI Search Engines: https://otterly.ai/blog/best-ai-search-monitoring-and-llm-monitoring-solutions/
  • AI Visibility Audit Service | Presenc AI: https://presenc.ai/use-cases/ai-visibility-audit-service
  • What Does an AI Visibility Audit Cost? A 2026 Pricing Guide: https://sparkletechnologies.com/blog/what-an-ai-visibility-audit-costs
  • SE Visible — An AI Visibility Tool Made to Empower Brands: https://visible.seranking.com/
  • 6 Best AI Search Agencies in 2026: https://www.gtm8020.com/blog/ai-search-agencies
  • Best AI SEO Agency | How to choose an AI SEO agency? - ZipTie.ai: https://ziptie.ai/blog/best-ai-seo-agency/
  • Additional AI research evidence136 records
    1. AI research evidence record deepseek:c1
    2. AI research evidence record kimi:c1
    3. AI research evidence record openai:becited_home
    4. AI research evidence record anthropic:4-2
    5. AI research evidence record google:1.2.3
    6. AI research evidence record grok:0
    7. AI research evidence record perplexity:c1
    8. AI research evidence record google:1.4.4
    9. AI research evidence record openai:becited_home
    10. AI research evidence record anthropic:4-2
    11. AI research evidence record anthropic:4-17
    12. AI research evidence record anthropic:4-20
    13. AI research evidence record anthropic:4-27
    14. AI research evidence record perplexity:c2
    15. AI research evidence record kimi:c1
    16. AI research evidence record perplexity:c1
    17. AI research evidence record grok:0
    18. AI research evidence record google:1.2.3
    19. AI research evidence record google:1.3.9
    20. AI research evidence record openai:becited_home
    21. AI research evidence record anthropic:4-2
    22. AI research evidence record grok:0
    23. AI research evidence record perplexity:c2
    24. AI research evidence record kimi:c1
    25. AI research evidence record google:1.2.3
    26. AI research evidence record anthropic:4-3
    27. AI research evidence record anthropic:28-3
    28. AI research evidence record anthropic:28-40
    29. AI research evidence record google:1.4.4
    30. AI research evidence record openai:becited_methodology
    31. AI research evidence record anthropic:4-17
    32. AI research evidence record anthropic:4-20
    33. AI research evidence record perplexity:c4
    34. AI research evidence record google:1.4.3
    35. AI research evidence record openai:becited_home
    36. AI research evidence record anthropic:4-4
    37. AI research evidence record deepseek:c1
    38. AI research evidence record grok:0
    39. AI research evidence record kimi:c1
    40. AI research evidence record openai:becited_case_studies
    41. AI research evidence record google:1.3.7
    42. AI research evidence record openai:becited_methodology
    43. AI research evidence record google:1.3.6
    44. AI research evidence record perplexity:c1
    45. AI research evidence record openai:becited_home
    46. AI research evidence record kimi:c1
    47. AI research evidence record kimi:c4
    48. AI research evidence record kimi:c5
    49. AI research evidence record kimi:c6
    50. AI research evidence record anthropic:4-2
    51. AI research evidence record openai:becited_methodology
    52. AI research evidence record perplexity:c3
    53. AI research evidence record perplexity:c4
    54. AI research evidence record google:1.4.3
    55. AI research evidence record google:1.2.7
    56. AI research evidence record anthropic:4-17
    57. AI research evidence record perplexity:c2
    58. AI research evidence record grok:0
    59. AI research evidence record google:1.4.4
    60. AI research evidence record anthropic:4-3
    61. AI research evidence record openai:becited_home
    62. AI research evidence record perplexity:c1
    63. AI research evidence record kimi:c1
    64. AI research evidence record grok:0
    65. AI research evidence record anthropic:4-4
    66. AI research evidence record google:1.2.3
    67. AI research evidence record deepseek:c1
    68. AI research evidence record perplexity:c2
    69. AI research evidence record openai:becited_terms
    70. AI research evidence record anthropic:5-2
    71. AI research evidence record anthropic:5-10
    72. AI research evidence record anthropic:8-5
    73. AI research evidence record anthropic:4-4
    74. AI research evidence record openai:becited_home
    75. AI research evidence record kimi:c1
    76. AI research evidence record anthropic:4-17
    77. AI research evidence record anthropic:4-27
    78. AI research evidence record perplexity:c2
    79. AI research evidence record anthropic:4-3
    80. AI research evidence record anthropic:28-40
    81. AI research evidence record openai:becited_home
    82. AI research evidence record anthropic:13-7
    83. AI research evidence record anthropic:22-4
    84. AI research evidence record kimi:c1
    85. AI research evidence record anthropic:4-3
    86. AI research evidence record anthropic:4-2
    87. AI research evidence record anthropic:23-1
    88. AI research evidence record anthropic:4-17
    89. AI research evidence record google:1.3.9
    90. AI research evidence record openai:becited_terms
    91. AI research evidence record perplexity:c1
    92. AI research evidence record anthropic:13-7
    93. AI research evidence record anthropic:22-4
    94. AI research evidence record kimi:c1
    95. AI research evidence record kimi:c2
    96. AI research evidence record kimi:c4
    97. AI research evidence record kimi:c6
    98. AI research evidence record anthropic:7-6
    99. AI research evidence record kimi:c3
    100. AI research evidence record kimi:c5
    101. AI research evidence record openai:becited_home
    102. AI research evidence record perplexity:c2
    103. AI research evidence record kimi:c1
    104. AI research evidence record grok:0
    105. AI research evidence record openai:becited_terms
    106. AI research evidence record anthropic:4-17
    107. AI research evidence record anthropic:4-4
    108. AI research evidence record openai:becited_case_studies
    109. AI research evidence record google:1.3.7
    110. AI research evidence record openai:becited_methodology
    111. AI research evidence record deepseek:c1
    112. AI research evidence record openai:becited_home
    113. AI research evidence record anthropic:4-2
    114. AI research evidence record google:1.2.3
    115. AI research evidence record grok:0
    116. AI research evidence record kimi:c1
    117. AI research evidence record perplexity:c1
    118. AI research evidence record deepseek:c1
    119. AI research evidence record anthropic:4-17
    120. AI research evidence record perplexity:c2
    121. AI research evidence record openai:becited_methodology
    122. AI research evidence record openai:becited_case_studies
    123. AI research evidence record google:1.3.7
    124. AI research evidence record deepseek:c1
    125. AI research evidence record openai:becited_home
    126. AI research evidence record anthropic:5-2
    127. AI research evidence record kimi:c2
    128. AI research evidence record kimi:c3
    129. AI research evidence record kimi:c4
    130. AI research evidence record kimi:c5
    131. AI research evidence record kimi:c6
    132. AI research evidence record anthropic:13-7
    133. AI research evidence record anthropic:22-4
    134. AI research evidence record openai:becited_case_studies
    135. AI research evidence record kimi:c1
    136. AI research evidence record google:1.3.7

Verify this research

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

Study date
September 18, 2026
Platforms analyzed
7
Source records
24
Ranking mentions
2 of 7
Platform share
29%
Final consensus rank
#6

Research trail and source mix

Configured platforms

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

Source mix

10 independent · 14 company-owned

Evidence support

15 direct · 9 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 04265ab7b7a023b8dcbfeed54232edfb41f8531ef27276363d1a86f8fef45521