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

AI Consensus Fit Review

Citable AI Search Agency Fit Review for Citation Architecture Strategy

Citable is a good fit for companies that need a citation architecture strategy built around owned content, entity signals, third-party sources, PR, community and profile surfaces.

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

Answer Capsule

Citable is a good fit for companies that need a citation architecture strategy built around owned content, entity signals, third-party sources, PR, community and profile surfaces. Two of seven platforms named Citable during the ranking stage, at an average listed rank of 3.0 and a best rank of 2. Its strongest asset is the published CITE Framework, which organizes work around Crawl access, Identity, Trust evidence and Extractability, backed by fixed-fee audits starting at €1,800. The main limitation is that public evidence is overwhelmingly company-authored, with no independently verified client outcomes, no guarantees, and unclear US-specific contract and publisher-execution terms.

Research Snapshot

FieldFinding
Platform mentions in ranking stage2 of 7 platforms (anthropic, grok)
Share of included platform responses28.6%
Average listed rank3.0
Best listed rank2
Relevant product/model/planCITE Framework Consulting Service; GEO-only specialist retainers
Overall use-case fitGood
Research date2026-09-18

Why Citable Qualified for This Study

Questions This Section Answers

  • Is Citable a good choice for AI Search Agencies for Citation Architecture Strategy?
  • How many AI platforms named Citable in the ranking stage for citation architecture strategy?

Citable qualified because two of the seven included platforms, anthropic and grok, named it during ranking discovery for this specific use case, meeting the two-mention minimum. Anthropic listed it at rank 4 and grok at rank 2, producing an average listed rank of 3.0 and a best rank of 2 [1].

The entity is a Spain-founded independent SEO and AI Search services agency that states US operating availability and describes its GEO surfaces and methodology [3]. Its published scope explicitly connects AI-answer measurement with owned-content structure, entity grounding, third-party sources, PR, communities and profile corrections [4].

Qualification required name normalization: company-name variants were collapsed onto one canonical brand before the minimum-mentions threshold was applied, which could mask other entities with similar names. Official-site retrieval also failed for one or more mentions during ranking-stage normalization, so no failed fetch was used as a verified domain key.

The Product, Model, Plan, or Service Most Relevant to AI Search Agencies for Citation Architecture Strategy

Questions This Section Answers

  • Which Citable plan should a buyer choose for citation architecture strategy if they need a prioritized source roadmap?
  • Is the CITE Framework Consulting Service a real Citable product, or should buyers ask for the AI Search Opportunity Audit instead?

The closest published service for this use case is the AI Search Opportunity Audit, a €3,800 fixed-fee engagement that includes cross-surface visibility and competitor analysis, technical, content, entity and off-site-source analysis, prioritized PR and third-party opportunities, and a 90-day roadmap [5]. The Program Audit at €5,400 adds governance, an experiment backlog, ticket architecture and one bounded implementation workstream [5].

The ranking-stage recommendation named a "CITE Framework Consulting Service" and "GEO-only specialist retainers," but that exact product name was not verified on the reviewed Citable service pages [7]. Citable's public pages more clearly describe Diagnose, Build and Operate services [8]. Buyers should confirm which named engagement they are actually purchasing.

The CITE Framework itself is published openly, and Citable asks only that users credit the agency and link back to its framework page when referencing it [12]. The four pillars map to Crawl access, Identity, Trust signals and Extractability [14].

What the AI Platforms Agreed About

Questions This Section Answers

  • What do AI platforms agree Citable does well for citation architecture strategy?
  • Does Citable measure citation outcomes rather than SEO rankings?

Platforms that evaluated Citable broadly agreed on its core positioning: it treats citation architecture as a distinct discipline from SEO, and it measures outcomes through citation presence rather than search position. Citable's own framework states that SEO ranks links in a results list while CITE optimizes for being cited inside an AI-synthesized answer, with success measured as Share-of-Answer per prompt across engines [15].

Platforms also converged on the diagnostic-first structure. Diagnose defines the commercial query panel and relevant surfaces, interprets visibility, narrative, competitor and source gaps, and prioritizes the decision [18]. Citable runs 50 prompts across 4 AI engines and scores each against all four CITE pillars, producing a baseline matrix showing which pillars are weakest, which prompts are missed, and which competitors win the toss-up [19].

A third area of agreement was the owned-versus-external source split. Citable states that AI citation work depends on owned content infrastructure and authoritative third-party citations, with off-site citation paths influencing whether brands are cited [21]. Independent commentary supports the general principle that owned content is necessary but not enough, and that if every claim about a brand only appears on its own domain, the evidence layer is thin [22].

Where the AI Platforms Disagreed or Were Uncertain

Questions This Section Answers

  • Is Citable's pricing and contract structure clear enough for a US buyer to budget without surprises?
  • How much independent evidence exists that Citable's citation architecture work produces client results?

The sharpest disagreement concerns verifiability. DeepSeek rated fit as uncertain because official-site retrieval failed during ranking-stage normalization, no independent corroboration was located, and pricing, contract length and deliverables were entirely unverified from public sources [25]. The other six platforms rated fit as good. Missing research is not the same as disagreement, but the gap is material for a buyer.

Pricing and terms are reported inconsistently across platforms. OpenAI and Grok describe fixed-fee audits at €1,800, €3,800 and €5,400 with implementation and monitoring scoped separately [26]. Anthropic reports an AI Visibility Audit at €1,200, GEO Foundations at €3,500–€5,500 per month with a 3-month minimum, GEO Growth at €3,500–€6,000 per month with a 6-month minimum, and entry-level retainers from €640 per month with a 30-day exit clause [28]. Perplexity reports GEO Foundations at €1,800 per month for 3 months and GEO Growth at €3,500–€6,000 per month with a 6-month minimum [33]. Kimi states that Operate retainer pricing is not publicly disclosed [34]. These figures do not reconcile cleanly, and buyers should treat published pricing as tier-dependent and confirm the exact engagement in writing.

Product naming is also unresolved. The recommended "CITE Framework Consulting Service" was not verified as a separately listed service [35], and Grok flagged the same mismatch [27]. Citable's own about page states it is not the CITABLE content framework published by Discovered Labs and has no affiliation with getcitable.com [36].

Company age and identity are a further uncertainty. Citable identifies itself as an independent agency founded in Tarragona, Spain, in 2026 [38]. It states US operating availability, but the depth of US staffing and local market execution is unclear [39]. No published 12+ month case studies showing sustained Share-of-Answer growth were located [38].

Use-Case-Specific Features and Capabilities

Questions This Section Answers

  • Does Citable's citation architecture work cover publishers, comparison sites and industry resources, or only owned content?
  • Which AI platforms does Citable monitor for citation presence?

Citable's published methodology treats owned pages, schema, crawler access, extractable structure, entity grounding and authoritative third-party citations as complementary layers [40]. The Context Hub model connects entity clarity, intent coverage, extractable answers and cross-surface citation paths [40].

Platform coverage is described across ChatGPT, Perplexity, Gemini and Google AI Overviews, with query panels, citation and source analysis, and narrative accuracy checks [40]. Google's evaluation adds that Citable's diagnostic assessments target ChatGPT, Claude, Gemini and Perplexity [44]. Exact platform coverage, prompt volume and access methods remain scope-dependent.

Off-site execution may include digital PR, communities, third-party profiles and entity corrections, but publication remains an editorial decision [41]. Citable's pricing page states that offsite authority is part of its existing GEO service scope, that Diagnose identifies PR, community, third-party profile and entity opportunities, and that the Program can include one agreed first offsite workstream (official:C2).

A disclosed gap matters here. Citable's public materials do not detail specific services or methodologies for publisher-architecture design, competitor monitoring or comparison-site positioning [45]. The buyer need explicitly includes publishers, comparison sites and industry resources, so this is a scope question to resolve directly rather than an established capability.

Pricing, Fees, Contracts, and Ongoing Costs

Questions This Section Answers

  • How much does Citable cost for citation architecture strategy, and are there setup or cancellation fees?
  • What is the minimum contract term for a Citable GEO retainer, and can a buyer exit early?

Published pricing is in EUR and tiered by audit, sprint and retainer. The clearest public figures are €1,200 for an AI Visibility Audit, €1,800 for a Baseline Review, €3,800 for an Opportunity Audit, €5,400 for a Program Audit, €1,800 per month for GEO Foundations over 3 months, and €3,500–€6,000 per month for GEO Growth with a 6-month minimum [46]. Citable's own pricing page lists the €1,800, €3,800 and €5,400 fixed-fee Diagnose tiers, Build project fees ranging from €2,500 to €18,000 depending on scope, SEO Continuity at €2,000–€3,000 per month, AI Search Delivery Assurance at €1,500–€3,000 per month, and workshops at €1,800 and €3,200 (official:C2, official:C3).

Additional fees are disclosed in several places. Production implementation is separate for Baseline and Opportunity tiers; build work, larger implementation, third-party spend, paid media placements, video production and large migrations may require separate scope or fees; and third-party publication and platform fees are not included unless explicitly scoped [49]. Ongoing monitoring is not included in any audit tier and must be scoped separately [50].

Contract terms are partially published. Citable states that each engagement has a defined scope, owner, price, dependencies and acceptance criteria [49]. Anthropic reports 30-day exit clauses on all retainers with no annual lock-in, and that if the agency is not delivering measurable value during the minimum term, the client can exit with 30 days' written notice and pay only for work completed [51]. GEO Foundations requires a 3-month minimum because the work is sequential, and GEO Growth and SEO Continuity retainers require 6-month minimums [53]. Perplexity reports that no detailed cancellation policy or auto-renewal terms were verified on the checked pages [46]. The public pricing reviewed does not state a standard minimum retainer term, cancellation notice, refund policy or US tax treatment [49].

Best Suited For

Questions This Section Answers

  • Who gets the most value from Citable for citation architecture strategy?
  • Is Citable a good fit for a company that already produces strong content but lacks off-site citation evidence?

Citable is best suited to companies that need a diagnostic baseline and a prioritized citation and source roadmap before committing to ongoing spend [55]. It fits organizations coordinating technical SEO, content, entity alignment and off-site authority work under one operating model [56].

It also suits buyers who want bounded implementation with explicit scope boundaries and governance, rather than open-ended retainers [55]. Growth-stage B2B and technology companies evaluating AI visibility across ChatGPT, Perplexity, Gemini and Google AI Overviews are a stated fit [59].

The specific buyer in this study, a company that already produces strong content but believes its AI visibility problem extends beyond its own website, is a reasonable match on paper, because Citable's published position is that on-site makes you readable while off-site makes you cited [60]. Buyers should confirm that publisher and comparison-site architecture work is actually in scope, since that is the least documented part of the offering [61].

Probably Not Best Suited For

Questions This Section Answers

  • Who should not choose Citable for citation architecture strategy?
  • Is Citable suitable for a buyer who needs guaranteed AI citations or large-scale publisher placements?

Citable is probably not the right choice for buyers seeking guaranteed inclusion in AI answers or guaranteed third-party publication. Citable states that it does not guarantee rankings, citations, recommendations, third-party publication or business outcomes because external models and retrieval systems change [62].

It is also a weaker fit for large enterprises requiring extensive publisher relations, paid placements, large migrations or always-on software monitoring [62]. Buyers requiring USD pricing or clearly published US-specific contract terms should look elsewhere or negotiate terms first [64].

Companies without an existing SEO foundation are a poor fit, because the CITE Framework assumes prior crawlability, schema and content infrastructure [65]. Buyers seeking revenue-attributed outcomes or managed service guarantees tied to inbound pipeline impact should also look elsewhere, since Citable measures Share of Answer growth rather than revenue attribution or MQL/SQL conversion [66].

When Another Option May Be Better

Questions This Section Answers

  • What is a better alternative to Citable for a buyer who needs high-volume publisher outreach and placement execution?
  • When should a buyer choose a monitoring platform or a US-based enterprise consultancy instead of Citable?

Choose a larger digital PR or authority-building agency when the main requirement is high-volume publisher outreach and placement execution [67]. Choose a specialized AI-search monitoring platform or agency-platform hybrid when continuous automated tracking, API access, dashboards and large prompt libraries are primary requirements [67].

Choose a US-based enterprise SEO/GEO consultancy when procurement, USD contracting, US references, local publisher relationships or enterprise implementation capacity are mandatory [67]. Choose an agency with independently documented client outcomes when proof of performance matters more than published methodology and scope transparency [67].

Other platform-reported alternatives include a full-service earned-media agency when publisher relationship management must sit inside a single contract, and a vertical specialist when the buyer needs deep B2B SaaS, healthcare or e-commerce experience rather than cross-vertical citation architecture [69]. Buyers needing month-to-month flexibility, bundled ongoing tracking or immediate lead generation should compare other options before committing [70].

Questions to Verify Before Buying

Questions This Section Answers

  • What should a buyer confirm with Citable before signing a contract for citation architecture strategy?
  • Can Citable provide US references or independently verifiable client outcomes?

Ask whether the engagement will deliver a source-by-source citation architecture map covering publishers, comparison sites, industry resources, authoritative domains, company assets and priority gaps [71]. Ask which AI platforms, regions, languages, prompt sets and retrieval modes are included, and how many queries will be tested [72].

Ask what exact off-site deliverables are included: profile corrections, digital PR strategy, outreach, community participation, publisher briefs or actual placement attempts [71]. Ask who owns outreach, content production, technical implementation, approvals and reporting, and what counts as completion and acceptance for the roadmap, implementation workstream and ongoing retainer [73].

Ask whether third-party fees, journalist databases, paid placements, travel, translation, taxes and platform costs are excluded [73]. Ask what the minimum term, renewal, cancellation, payment and refund provisions are for recurring work [73].

Ask whether Citable can provide relevant US references, anonymized deliverables or independently verifiable client outcomes [75]. Ask how citation share, source quality, narrative accuracy, recommendation frequency and business impact will be measured without implying causation [71]. Finally, ask whether the proposed engagement is actually the CITE Framework Consulting Service, an AI Search Opportunity Audit, a Program Audit or a custom GEO retainer [76].

Final AI Consensus Verdict

Citable is a good fit for citation architecture strategy, with a clear caveat about evidence quality. Six of seven platforms rated fit as good; DeepSeek rated it uncertain because it could not retrieve the official site or locate independent corroboration [77]. The strongest reason to consider Citable is that its published scope explicitly connects AI-answer measurement with owned-content structure, entity grounding, third-party sources, PR, communities and profile corrections, and it publishes fixed-fee entry points [78].

The main limitation is that public evidence is primarily company-authored. Independent customer evidence, case studies and outcome validation were not established in the sources reviewed [80]. Citable offers no guarantee of citations, recommendations, publication or revenue impact [78]. Publisher outreach scale, relationships, placement volume, geographic coverage and success criteria are not publicly specified [78].

Treat Citable as a boutique, Spain-based services agency with limited independently verified outcome evidence, no guarantees, and unclear US-specific contract and publisher-execution details [80]. The strongest match is the €3,800 AI Search Opportunity Audit for strategy and the €5,400 Program Audit when one bounded implementation workstream is needed [79]. Buyers comparing this option against the wider field can review the full AI Search Agencies for Citation Architecture Strategy index before deciding.

How This Review Was Produced

This review synthesizes platform-reported fit research from seven AI platforms: OpenAI, Anthropic, Google, Grok, Perplexity, Kimi and DeepSeek. Each platform evaluated Citable against the citation architecture strategy use case and supplied citations, pricing details, limitations and verification questions. The authoritative research date is 2026-09-18.

Platform-reported research dates differ from the authoritative run date: OpenAI reported 2026-09-17 and DeepSeek reported 2026-06-01, while the remaining platforms reported 2026-09-18. These are provenance metadata and do not independently prove freshness.

All included platforms evaluated fit, but the platform-mentions count reflects only the two platforms that named Citable during ranking discovery. Company-owned citations materially outnumber independent citations in the underlying research, so company claims should not be read as independently verified. The supplied URLs were collected from platform responses and were not independently validated at the writing stage. Citations are platform-reported evidence, not independently verified facts.

Methodology Limitations

Several limitations apply. First, the ranking-stage mention count is low: only two of seven platforms named Citable, so the consensus base is narrow. Second, official-site retrieval failed for one or more mentions during ranking-stage normalization, and no failed fetch was used as a verified domain key. Third, company-name variants were collapsed onto one canonical brand before the minimum-mentions threshold was applied, which could mask other entities with similar names.

Fourth, pricing and product naming conflict across platforms and are not fully reconciled. The recommended "CITE Framework Consulting Service" was not verified as a separately listed service [81]. Fifth, no independently verified client outcomes, long-term case studies or US-market references were located. Sixth, platform-reported research dates differ from the authoritative run date. Seventh, the CITE Framework is described as a published review structure and not a causal model or outcome guarantee [82]. Eighth, independent academic work distinguishes citation selection from citation absorption, supporting the need to verify both source selection and how cited pages influence answers, but it does not validate Citable's commercial outcomes [83].

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

Sources

Company-Owned Sources

  • SEO, GEO & AI Search Agency: https://citable.agency/
  • About Citable | SEO & AI Search Services Agency: https://citable.agency/about/
  • AI Visibility Audit | 1,200 EUR | Citable: https://citable.agency/audit/
  • The CITE Framework — How AI Cites Brands | Citable: https://citable.agency/framework
  • GEO Journal — Field Notes & Research | Citable: https://citable.agency/journal
  • GEO pricing in 2026: what AI search optimization actually costs | Citable: https://citable.agency/journal/geo-pricing-2026/
  • On-Site Makes You Readable. Off-Site Makes You Cited: https://citable.agency/journal/readable-vs-cited/
  • What is a Context Hub? The GEO content structure built for AI citation: https://citable.agency/journal/what-is-a-context-hub/
  • SEO & AI Search Pricing: Audits & Implementation: https://citable.agency/pricing/
  • Official pricing and terms source: https://citable.agency/pricing/#operate
  • Official pricing and terms source: https://citable.agency/pricing/#enable
  • Additional AI research evidence83 records
    1. AI research evidence record anthropic:17-19
    2. AI research evidence record grok:web:0
    3. AI research evidence record openai:c4
    4. AI research evidence record openai:c3
    5. AI research evidence record openai:c2
    6. AI research evidence record openai:c3
    7. AI research evidence record openai:c6
    8. AI research evidence record anthropic:31-5
    9. AI research evidence record anthropic:33-10
    10. AI research evidence record anthropic:33-11
    11. AI research evidence record anthropic:33-12
    12. AI research evidence record anthropic:17-21
    13. AI research evidence record anthropic:17-22
    14. AI research evidence record anthropic:36-1
    15. AI research evidence record anthropic:17-23
    16. AI research evidence record anthropic:17-24
    17. AI research evidence record anthropic:17-25
    18. AI research evidence record anthropic:33-14
    19. AI research evidence record anthropic:17-8
    20. AI research evidence record anthropic:17-9
    21. AI research evidence record openai:c5
    22. AI research evidence record anthropic:8-4
    23. AI research evidence record anthropic:8-5
    24. AI research evidence record anthropic:8-6
    25. AI research evidence record deepseek:c1
    26. AI research evidence record openai:c2
    27. AI research evidence record grok:web:0
    28. AI research evidence record anthropic:28-11
    29. AI research evidence record anthropic:29-3
    30. AI research evidence record anthropic:29-15
    31. AI research evidence record anthropic:29-25
    32. AI research evidence record anthropic:29-26
    33. AI research evidence record perplexity:c1
    34. AI research evidence record kimi:citable-agency-1
    35. AI research evidence record openai:c6
    36. AI research evidence record anthropic:31-9
    37. AI research evidence record anthropic:31-10
    38. AI research evidence record anthropic:31-1
    39. AI research evidence record openai:c4
    40. AI research evidence record openai:c1
    41. AI research evidence record openai:c3
    42. AI research evidence record openai:c5
    43. AI research evidence record openai:c4
    44. AI research evidence record google:1.2.1
    45. AI research evidence record anthropic:17-19
    46. AI research evidence record perplexity:c1
    47. AI research evidence record perplexity:c2
    48. AI research evidence record anthropic:28-11
    49. AI research evidence record openai:c2
    50. AI research evidence record kimi:citable-agency-1
    51. AI research evidence record anthropic:29-15
    52. AI research evidence record anthropic:29-27
    53. AI research evidence record anthropic:29-25
    54. AI research evidence record anthropic:29-26
    55. AI research evidence record openai:c3
    56. AI research evidence record anthropic:31-3
    57. AI research evidence record anthropic:31-4
    58. AI research evidence record kimi:citable-agency-1
    59. AI research evidence record anthropic:33-1
    60. AI research evidence record openai:c5
    61. AI research evidence record anthropic:17-19
    62. AI research evidence record openai:c3
    63. AI research evidence record anthropic:33-1
    64. AI research evidence record openai:c4
    65. AI research evidence record anthropic:17-19
    66. AI research evidence record anthropic:17-25
    67. AI research evidence record openai:c3
    68. AI research evidence record openai:c4
    69. AI research evidence record anthropic:17-19
    70. AI research evidence record kimi:citable-agency-1
    71. AI research evidence record openai:c3
    72. AI research evidence record openai:c1
    73. AI research evidence record openai:c2
    74. AI research evidence record perplexity:c1
    75. AI research evidence record openai:c4
    76. AI research evidence record openai:c6
    77. AI research evidence record deepseek:c1
    78. AI research evidence record openai:c3
    79. AI research evidence record openai:c2
    80. AI research evidence record openai:c4
    81. AI research evidence record openai:c6
    82. AI research evidence record perplexity:c7
    83. AI research evidence record openai:c7

Independent Sources

Other Sources

  • AI Citation Services — Stay Citable | Full AI Citation Stack: https://www.staycitable.com/services
  • Additional AI research evidence83 records
    1. AI research evidence record anthropic:17-19
    2. AI research evidence record grok:web:0
    3. AI research evidence record openai:c4
    4. AI research evidence record openai:c3
    5. AI research evidence record openai:c2
    6. AI research evidence record openai:c3
    7. AI research evidence record openai:c6
    8. AI research evidence record anthropic:31-5
    9. AI research evidence record anthropic:33-10
    10. AI research evidence record anthropic:33-11
    11. AI research evidence record anthropic:33-12
    12. AI research evidence record anthropic:17-21
    13. AI research evidence record anthropic:17-22
    14. AI research evidence record anthropic:36-1
    15. AI research evidence record anthropic:17-23
    16. AI research evidence record anthropic:17-24
    17. AI research evidence record anthropic:17-25
    18. AI research evidence record anthropic:33-14
    19. AI research evidence record anthropic:17-8
    20. AI research evidence record anthropic:17-9
    21. AI research evidence record openai:c5
    22. AI research evidence record anthropic:8-4
    23. AI research evidence record anthropic:8-5
    24. AI research evidence record anthropic:8-6
    25. AI research evidence record deepseek:c1
    26. AI research evidence record openai:c2
    27. AI research evidence record grok:web:0
    28. AI research evidence record anthropic:28-11
    29. AI research evidence record anthropic:29-3
    30. AI research evidence record anthropic:29-15
    31. AI research evidence record anthropic:29-25
    32. AI research evidence record anthropic:29-26
    33. AI research evidence record perplexity:c1
    34. AI research evidence record kimi:citable-agency-1
    35. AI research evidence record openai:c6
    36. AI research evidence record anthropic:31-9
    37. AI research evidence record anthropic:31-10
    38. AI research evidence record anthropic:31-1
    39. AI research evidence record openai:c4
    40. AI research evidence record openai:c1
    41. AI research evidence record openai:c3
    42. AI research evidence record openai:c5
    43. AI research evidence record openai:c4
    44. AI research evidence record google:1.2.1
    45. AI research evidence record anthropic:17-19
    46. AI research evidence record perplexity:c1
    47. AI research evidence record perplexity:c2
    48. AI research evidence record anthropic:28-11
    49. AI research evidence record openai:c2
    50. AI research evidence record kimi:citable-agency-1
    51. AI research evidence record anthropic:29-15
    52. AI research evidence record anthropic:29-27
    53. AI research evidence record anthropic:29-25
    54. AI research evidence record anthropic:29-26
    55. AI research evidence record openai:c3
    56. AI research evidence record anthropic:31-3
    57. AI research evidence record anthropic:31-4
    58. AI research evidence record kimi:citable-agency-1
    59. AI research evidence record anthropic:33-1
    60. AI research evidence record openai:c5
    61. AI research evidence record anthropic:17-19
    62. AI research evidence record openai:c3
    63. AI research evidence record anthropic:33-1
    64. AI research evidence record openai:c4
    65. AI research evidence record anthropic:17-19
    66. AI research evidence record anthropic:17-25
    67. AI research evidence record openai:c3
    68. AI research evidence record openai:c4
    69. AI research evidence record anthropic:17-19
    70. AI research evidence record kimi:citable-agency-1
    71. AI research evidence record openai:c3
    72. AI research evidence record openai:c1
    73. AI research evidence record openai:c2
    74. AI research evidence record perplexity:c1
    75. AI research evidence record openai:c4
    76. AI research evidence record openai:c6
    77. AI research evidence record deepseek:c1
    78. AI research evidence record openai:c3
    79. AI research evidence record openai:c2
    80. AI research evidence record openai:c4
    81. AI research evidence record openai:c6
    82. AI research evidence record perplexity:c7
    83. AI research evidence record openai:c7

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
22
Ranking mentions
2 of 7
Platform share
29%
Final consensus rank
#5

Research trail and source mix

Configured platforms

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

Source mix

8 independent · 13 company-owned · 1 unclear

Evidence support

17 direct · 5 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 3c6203073992b503afeb3025892292eb3c012e9399637ac10a7bee4cad9a10b9