Maya AI Visibility Platform Fit Review for Historical Trend Tracking

Answer Capsule

Maya is a good fit for AI Visibility Platforms for Historical Trend Tracking, with a material verification requirement. Two of seven platforms named Maya during the ranking stage (google, kimi), a 28.6% share of included platform responses, at an average listed rank of 5.5 and a best rank of 3. The strongest reason to consider it: Maya publicly documents time series, weekly topic snapshots, retrospective traceability, and indefinite historical retention, which map directly to the use case. The main limitation: snapshot immutability, retention mechanics, export rights, and pricing are inconsistent or undisclosed across public pages, so durability claims should be confirmed contractually before purchase.

Research Snapshot

Why Maya Qualified for This Study

Questions This Section Answers

• Is Maya a good choice for AI Visibility Platforms for Historical Trend Tracking?
• How many AI platforms named Maya in the ranking stage for historical trend tracking?

Maya qualified because it cleared the study's minimum-mention threshold and because its public materials address the specific criteria this use case requires: historical recommendation data, citation trends, competitor movement, prompt-level changes, platform differences, and snapshots that are not overwritten by newer results.

Two of the seven included platforms named Maya during ranking discovery: google (rank 3) and kimi (rank 8). That is a 28.6% share of included platform responses, an average listed rank of 5.5, and a best listed rank of 3. Five platforms evaluated Maya's fit but did not name it in their ranking output, so the mention count is lower than the evaluation count.

Maya's qualification rests on company-owned documentation rather than independent verification. Its site states that it tracks brand mentions, citations, competitors, prompt activity, source analysis, traffic context, and progress over time across supported AI platforms ^[1]. Its methodology page documents retrospective traceability, measurement scope, response and evaluation units, citation analysis, competitor grouping, and comparability limits ^[2]. Its AI Visibility Tracking page states that it monitors selected prompts, compares enabled providers, records responses, checks brand mentions, and provides answer-level evidence ^[3].

The study's ranking stage and its fit-evaluation stage produced different signals. Google rated Maya a strong fit; openai, anthropic, grok, and perplexity rated it good; deepseek and kimi rated it uncertain. That spread is itself a finding: the platforms that retrieved Maya's own pages found direct evidence, while the platforms that could not retrieve them treated the product as unverified.

The Product, Model, Plan, or Service Most Relevant to AI Visibility Platforms for Historical Trend Tracking

Questions This Section Answers

• Which Maya plan should a buyer choose if they need broad historical trend tracking across multiple AI platforms?
• Does Maya's Starter plan support historical trend tracking across more than one AI platform?

The relevant product is Maya AI Visibility Tracking, marketed around time series and weekly topic snapshots rather than single-day verdicts ^[4]. For broad historical trend tracking, the Enterprise plan is the most relevant publicly described option, because multi-platform coverage and higher scan frequency sit behind it ^[6].

Maya's public plan structure separates historical breadth by tier. Starter tracks ChatGPT only; Premium covers a limited platform set; Enterprise covers all listed platforms ^[7]. Enterprise is publicly described with all listed AI platforms, unlimited projects and seats, 400+ prompts, 30,000+ AI responses per month, up to four daily scans, API access, Looker Studio integration, dedicated support, and custom reporting ^[6]. Google's research describes the same tier as unlimited projects and seats, all platforms, and 400+ prompt runs per month ^[8].

The platform list Maya publishes at Enterprise level includes ChatGPT, Claude, Gemini, Perplexity, Google AI Overview, Google AI Mode, Copilot, Grok, and DeepSeek ^[6]. A separate Maya page describes querying "every major LLM — ChatGPT, Claude, Gemini, Perplexity, Grok, DeepSeek & more" ^[9], and another states thousands of queries across 7+ AI platforms daily ^[10]. The exact count therefore varies by page and by plan, which is a disclosure problem rather than a capability claim that can be resolved here.

For buyers whose historical tracking need is multi-platform, the practical reading is that Starter cannot deliver it. Starter is limited to ChatGPT according to the public pricing page ^[6], and Google's research states the same restriction explicitly ^[8].

What the AI Platforms Agreed About

Questions This Section Answers

• What do the AI platforms agree Maya does well for historical AI visibility tracking?
• Does Maya publicly document citation trend tracking and competitor movement over time?

Agreement was strong but not unanimous on four points, and every one of them traces back to Maya's own pages rather than to independent testing.

First, historical retention. Multiple platforms reported that Maya states historical data is retained indefinitely for long-term trend analysis and competitive benchmarking ^[12]. Perplexity reported the same claim from Maya's tracking page ^[15]. This is a company claim, not an independently verified retention guarantee.

Second, snapshot architecture. Maya states it shows time series and weekly topic snapshots instead of single-day verdicts, and never backfills invented history when a new engine is added ^[16]. The non-backfill policy is the closest thing in the public record to a snapshot-integrity commitment, and it was reported consistently across platforms.

Third, citation and competitor tracking. Maya states it tracks citations and source pages, distinguishes citation records from unique sources and from responses containing a source, and supports historical review of cited pages ^[19]. It advertises competitive share of voice, competitor comparisons, competitor content and citation gaps, and progress tracked over time ^[19]. Anthropic's research adds that every plan includes competitor tracking, with visibility scores, rankings, and share of voice shown alongside the buyer's own ^[21].

Fourth, prompt-level monitoring. Maya states it monitors selected customer questions, compares results across enabled providers, and provides answer-level evidence ^[24]. It advises keeping prompts, models, and target markets consistent when comparing periods ^[24]. Anthropic's research describes a fixed prompt set per market run daily under consistent conditions, with the stated goal that a score movement reflects a trend rather than model noise or a rephrased question ^[25].

Where the AI Platforms Disagreed or Were Uncertain

Questions This Section Answers

• Why did some AI platforms rate Maya uncertain for historical trend tracking?
• Is Maya's historical data retention independently verified or only company-reported?

The disagreement is not about whether Maya exists or what category it occupies. It is about whether the historical guarantees are verifiable.

Kimi rated Maya uncertain and reported that the domain was inaccessible during its research, that no cached or archived versions were found, and that no independent source confirmed Maya exists as an AI visibility platform rather than another product sharing the name ^[27]. Kimi also noted that multiple unrelated products named Maya exist, including Autodesk 3D software and a Crayon Data platform, causing confusion in pricing and review aggregators ^[27]. Anthropic's research independently flagged the same naming collision, noting that internal company reviews on Capterra and GetApp reference unrelated maya.ai products ^[28].

Deepseek rated Maya uncertain for a different reason: it found no independent corroboration of historical retention, snapshot immutability, pricing, or platform coverage, and treated those as unknown rather than false ^[29]. Deepseek's research ran without search enabled and is dated 2026-01-15, eight months before the study date, so its uncertainty reflects a narrower evidence base rather than a contradiction of the other platforms.

Pricing is the sharpest documented conflict. Maya's pricing page shows Starter at $99/month and Premium at $399/month ^[30]. A separate Maya blog page lists $99 starter, $399 pro, and $899 enterprise ^[33]. A different site version mentions early access pricing around $18/month ^[34]. Perplexity reported all three and concluded that public pricing is inconsistent across Maya pages ^[33].

Platform coverage conflicts too. Maya's pricing page description says Premium includes four AI platforms of choice, while the comparison text on the same page says two ^[30]. Anthropic's research reports Premium as any two platforms of choice ^[35], while Google's research reports Premium as four platforms of choice ^[32]. Both readings trace to Maya's own materials.

Retention mechanics remain unspecified. The public materials describe retrospective traceability but do not state how long historical records are retained, whether snapshots are immutable, versioned, exportable in raw form, or protected from overwrite, or how model-version metadata, sampling, retries, and failed responses are handled ^[36]. Anthropic's research adds that the indefinite-retention claim lacks technical specification of whether it covers actual response snapshots, metadata only, or tiered archival ^[28].

Use-Case-Specific Features and Capabilities

Questions This Section Answers

• Does Maya track prompt-level changes and platform differences for historical trend analysis?
• Can Maya show competitor movement and share of voice over weeks and months?

Maya's public feature set maps to five of the six criteria in this use case, with the sixth — reliable snapshots that are not overwritten — only partially addressed.

Historical recommendation and answer data: Maya states it records responses to monitored prompts and supports retrospective examination of a past observation together with the question, platform, date, answer passage, and source links ^[37]. Retention duration and immutability are not specified publicly.

Citation trends and source analysis: the platform tracks citations and source pages, distinguishes citation records from unique sources and from responses containing a source, and supports historical review of cited pages ^[39]. Anthropic's research adds that Maya maps every source AI cites about a space, showing which sites mention the brand versus competitors and which content types each model draws on ^[40].

Competitor movement and share of voice: Maya advertises competitive share of voice, competitor comparisons, competitor content and citation gaps, and progress tracked over time ^[39]. Its methodology states that competitor selection, brand grouping, and denominators affect comparisons, so trend reports require stable competitor definitions ^[38]. Google's research describes share-of-voice, ranking context, and competitor-only mentions monitored over weeks and months ^[41].

Prompt-level changes: AI Visibility Tracking monitors selected customer questions, compares results across enabled providers, and provides answer-level evidence ^[37]. Maya supports custom prompt creation, organization, and A/B testing at scale, and auto-discovers relevant queries per industry ^[42]. Anthropic's research notes that the depth of prompt-level change detection relative to competing platforms is not specified in available sources ^[42].

Platform differences: Maya supports cross-provider comparison, with public plan materials listing ChatGPT, Claude, Gemini, Perplexity, Google AI Overview, Google AI Mode, Copilot, Grok, and DeepSeek at Enterprise level ^[44]. Maya states that results from one platform should not be treated as the exact experience of another ^[38]. Anthropic's research adds multi-language query execution, with queries run in the language buyers actually use rather than translated English prompts ^[45].

Snapshot reliability: this is the weakest area. Maya documents retrospective traceability and warns that question selection, platform updates, language, geography, answer variability, denominators, and unknown model or personalization settings can affect trends ^[38]. Public materials do not verify immutable snapshot storage, complete model-version labeling, or protection against historical result replacement ^[38]. The non-backfill commitment is the strongest public statement in this area ^[47].

Scope limits apply to every one of these features. Maya explicitly limits measurement to selected questions and responses rather than every user question or total market demand, and treats visibility, citations, recommendations, traffic, conversions, and market share as distinct measurements ^[39]. Perplexity's research reports the same scope caveat from Maya's own site ^[49].

Pricing, Fees, Contracts, and Ongoing Costs

Questions This Section Answers

• How much does Maya cost per month, and are there setup or cancellation fees?
• What should a buyer confirm about Maya's annual billing, renewal, and refund terms before signing?

Maya's public pricing is partially disclosed and internally inconsistent. The clearest figures come from the pricing page: Starter at $99/month and Premium at $399/month on monthly billing, with a yearly option displayed as 17% lower, and Enterprise priced custom ^[50]. Google's research reports the same two figures and describes Enterprise as custom ^[51]. Perplexity's research reports the same starting point but also found a Maya blog page listing $99 starter, $399 pro, and $899 enterprise, plus a separate site version mentioning early access pricing around $18/month ^[52].

Plan contents as publicly described:

Trial and cancellation: the pricing page states a 3-day free trial, no credit card required, and cancellation during the trial without charge ^[50]. Monthly versus yearly commitment mechanics, post-trial cancellation rules, refunds, auto-renewal, data-retention periods, and deletion terms are unclear from the reviewed public pricing page ^[50]. Anthropic's research reports no contract term or commitment period publicly specified and no multi-year discount or annual-versus-monthly comparison available in search results ^[57].

Additional fees: no separate public fee schedule was identified for additional prompts, response volume, API usage, data exports, historical retention, or extra platform coverage ^[50]. Anthropic's research reports no per-engine add-on fees documented and no hidden per-query charges on the visible pricing page ^[57]. Enterprise custom integrations, reporting, support, and other negotiated services may affect total cost and should be verified commercially ^[50].

Pricing confidence is low to moderate across platforms. OpenAI rated its pricing confidence moderate ^[50]; Anthropic and Perplexity both rated theirs low ^[57]. Google rated its pricing confidence high ^[51], which conflicts with the other platforms' assessments and with the documented cross-page inconsistencies.

Best Suited For

Questions This Section Answers

• Who gets the most value from Maya for historical AI visibility trend tracking?
• Is Maya a good fit for agencies tracking competitor share of voice across multiple AI platforms?

Maya is best suited to companies establishing a repeatable baseline across selected prompts and AI platforms, and to teams comparing brand mentions, citations, sentiment, share of voice, and competitors over recurring runs ^[59]. Enterprise buyers needing broad platform coverage, higher scan frequency, API access, reporting, and custom support are the clearest fit ^[60].

Anthropic's research frames the same buyer more specifically: companies tracking brand visibility across multiple AI platforms who need to prove visibility trends over months or years, marketing teams needing competitor movement and share-of-voice trends alongside their own citation patterns, organizations seeking platform-level differences in how AI engines cite their content, and brands connecting visibility changes to specific content or metadata updates over time ^[61].

Google's research adds agencies and brands prioritizing generative engine optimization, and notes that unlimited seats across all paid tiers makes the platform collaborative for agency teams ^[63]. Grok's research points to brands needing tracked prompt responses over time with answer-level evidence, and users prioritizing model comparisons and share-of-voice trends ^[64].

A practical note for this buyer segment: the historical features that matter most for trend work — multi-platform coverage, higher scan frequency, topic visibility trends, and API access — are concentrated in Enterprise ^[60]. Buyers evaluating Starter or Premium for historical trend tracking should confirm which trend features their tier actually includes.

Probably Not Best Suited For

Questions This Section Answers

• Who should not choose Maya for historical AI visibility trend tracking?
• Is Maya suitable for buyers who need audit-grade immutable historical snapshots?

Maya is probably not the right choice for buyers requiring publicly documented unlimited historical retention or legally and audit-grade immutable snapshots ^[67]. The public materials do not verify immutable snapshot storage, complete model-version labeling, or protection against historical result replacement ^[67].

It is also a poor fit for buyers needing guaranteed reconstruction of every real-user answer or market-wide AI recommendation behavior. Maya explicitly limits measurement to selected questions and responses rather than every user question or total market demand ^[68].

Small buyers needing multiple AI platforms and extensive historical volume at a low fixed price should look elsewhere. Starter is limited to ChatGPT, so entry-level cross-engine historical comparison is not available ^[70]. Anthropic's research adds that budget-constrained buyers seeking entry-level tools under $50/month are not well served, and that teams requiring real-time or sub-daily updates may find the daily cadence insufficient ^[72].

Teams needing deep native integration with GA4, GSC, or CRM platforms for attribution should also verify carefully. Anthropic's research reports no documented native integrations with those systems, requiring manual dashboard consolidation to connect visibility trends to conversion outcomes ^[72]. Deepseek's research adds that procurement teams requiring published enterprise pricing, SLAs, or third-party security certifications should treat Maya as unverified ^[73].

When Another Option May Be Better

Questions This Section Answers

• What is a better alternative to Maya for a buyer who needs documented immutable historical snapshots?
• When should a buyer choose a cheaper or more specialized AI visibility tool over Maya?

Another option may be better in several specific situations, and the alternatives below come from platform-reported research rather than independent testing.

When contractually defined immutable historical snapshots, guaranteed retention periods, complete raw-response exports, or independently auditable collection logs are required, choose another platform ^[74]. When verified coverage of a specific AI search or recommendation surface that Maya has not confirmed for the target market is needed, choose another platform ^[75].

When budget is the constraint, lower-cost options exist. Anthropic's research cites Akii from $49/month and OtterlyAI from $29/month as alternatives for buyers under $50/month ^[76]. Kimi's research names Presenc AI for indefinite daily snapshots with annotated event overlays and prompt-level history with CSV/PNG/API export, Viali for per-query visibility across six engines with verbatim answer text every six hours, optiseo for official API-based tracking at $79–149/month, Wellows for daily citation snapshots with prompt-level diffs, SE Visible for real browser-based collection at $99/month for 200 prompts, Meev for continuous share-of-answer tracking across eight engines, and Rankscale for 17+ engine coverage with citation frequency monitoring ^[77].

When advanced statistical trend decomposition is required, Anthropic's research notes that Profound explicitly offers trend decomposition while Maya's approach is not independently confirmed ^[84]. When deep native CRM or marketing automation integration is mandatory, HubSpot AEO and Omnia are cited as offering broader integration ecosystems ^[84]. When real-time or sub-hourly updates are needed for rapid competitive monitoring, Maya's daily cadence may lag ^[85].

When the buyer needs a specialized analytics or data-warehouse workflow for long-term archival, custom statistical normalization, or organization-wide historical data governance, a dedicated analytics stack is the better path ^[74].

Questions to Verify Before Buying

These questions come from the platforms' own verification lists and should be answered in writing before signing.

Retention and immutability: Are historical responses, citations, screenshots, prompt versions, platform versions, timestamps, and geographic settings stored indefinitely or for a defined retention period? Are historical records immutable and append-only, or can reruns overwrite prior results ^[86]? What is the technical architecture of "indefinite" retention — are response snapshots compressed after six months, rolled into weekly aggregates, or stored at full granularity indefinitely ^[87]?

Export and access: Can the buyer export raw answers, citations, metadata, screenshots, and time-series data through CSV, API, or a warehouse connector ^[86]? Can historical snapshots be exported in bulk for BI tool integration or data warehouse ingestion ^[88]? Does Maya offer API access to historical trend data, or is all analysis dashboard-only ^[88]?

Collection methodology: Which exact models, interfaces, regions, languages, personas, and search settings are used for each supported platform ^[86]? How are failed responses, rate limits, retries, duplicate runs, cached responses, and platform outages handled in trend calculations ^[86]? If daily queries to a platform fail on a given day, is that data interpolated, marked as missing, or retried ^[88]?

Plan scope: Which Premium platform-count statement is contractually correct — two or four platforms of choice ^[89]? Does Enterprise include all advertised platforms and up to four daily scans without volume-based overages ^[89]? Does Premium's platform constraint limit competitor tracking to those same platforms, or can competitors be tracked across all platforms ^[88]?

Commercial terms: What are the annual commitment, auto-renewal, cancellation, refund, data deletion, and post-cancellation export terms ^[89]? Which exact plan includes time series, weekly snapshots, and indefinite historical retention ^[90]? Which public pricing figure is current — $99/$399, $99/$399/$899, or the roughly $18/month early access figure ^[91]?

Evidence: Can Maya provide a sample longitudinal report showing prompt-level changes, competitor movement, citations, model and platform differences, and preserved historical snapshots ^[86]?

Final AI Consensus Verdict

Maya is a good fit for AI Visibility Platforms for Historical Trend Tracking, with a material verification requirement. The consensus is not unanimous: Google rated it strong, openai, anthropic, grok, and perplexity rated it good, and deepseek and kimi rated it uncertain. Two of seven platforms named it during ranking discovery.

The case for Maya rests on unusually direct public documentation for this specific use case: prompt-based monitoring, cross-platform comparison, citation analysis, competitor trends, retrospective traceability, time series and weekly snapshots, and a stated policy against backfilling invented history ^[93]. The case against treating it as settled rests on what the public record does not show: retention duration, immutable snapshot guarantees, exportable historical records, complete platform and model-version controls, and consistent pricing ^[94].

The practical verdict for a buyer: shortlist Maya for historical AI visibility tracking, weight Enterprise if multi-platform history is the requirement, and do not treat snapshot durability, retention, reproducibility, or historical data-governance behavior as verified until it is documented contractually. The full field of options for this use case is compared in the AI Visibility Platforms for Historical Trend Tracking consensus index, and broader category coverage sits in the ai visibility llm monitoring directory.

How This Review Was Produced

This review evaluates Maya only for the AI Visibility Platforms for Historical Trend Tracking use case. It is not a broad company review and does not assess Maya outside this scope.

Seven platforms evaluated Maya's fit for this use case: openai, anthropic, google, grok, perplexity, deepseek, and kimi. Two of those seven named Maya during the ranking stage. The study research date is 2026-09-19. Platform-reported research dates are provenance metadata and do not independently prove freshness; deepseek's research is dated 2026-01-15 and ran without search enabled, so its findings reflect a narrower evidence base.

All factual claims in this review are attributed to the platform that reported them. Company-owned citations materially outnumber independent citations in the supplied evidence, so company claims are described as company claims rather than independently verified facts. The supplied URLs were collected from platform responses and were not independently validated at the writing stage. Where platforms disagreed, the disagreement is preserved rather than resolved.

Methodology Limitations

Several limitations apply to this review and should be weighed before purchase.

Evidence ownership is skewed. Of the deduplicated sources, 17 are company-owned and 3 are independent. Maya's historical retention, snapshot architecture, and platform coverage claims come almost entirely from Maya's own pages, so they are company-reported rather than independently verified.

Platform research dates differ from the study date. Deepseek's research is dated 2026-01-15, eight months before the 2026-09-19 study date, and ran without search enabled. Its uncertainty about Maya reflects a narrower evidence base and should not be read as a contradiction of platforms that retrieved Maya's pages.

Pricing is unresolved. Maya's public pages show conflicting figures, including $99/$399, $99/$399/$899, and roughly $18/month early access pricing ^[100]. Platform coverage for Premium is described as both two and four platforms of choice within Maya's own materials ^[102]. This review does not resolve those conflicts.

Naming collisions affect the evidence base. Multiple unrelated products named Maya exist, including Autodesk 3D software and a Crayon Data platform, and these appear in review aggregators alongside the AI visibility product ^[105]. Kimi reported that the withmaya.ai domain was inaccessible during its research and that no cached or archived versions were found ^[105].

Snapshot durability is unverified. No supplied source independently confirms immutable snapshot storage, retention duration, raw export rights, or protection against historical result replacement ^[107].

Agreement among AI platforms does not establish product quality. The fit ratings in this review reflect what each platform could retrieve and assess on the study date, not verified performance.