Your marketing team already has a weekly dashboard. It shows search performance, campaigns and website traffic, and now stakeholders want to see how the brand appears in ChatGPT, Google AI Mode and other AI Search platforms. The question is how to bring useful visibility, competitor and citation data into that report without rebuilding your reporting stack.
An API can help, but it is only one route. A native report may cover the task, an export may be enough for a monthly presentation, and a Looker Studio connector can supply a recurring dashboard. An MCP connection lets an AI assistant investigate the same analytics conversationally. The right choice depends on the information each route actually exposes and the subscription needed to access it.
This guide compares Superlines, Profound, Semrush, Ahrefs Brand Radar, Peec AI and Searchable for external marketing reporting and analysis. Superlines publishes this guide and is one of the providers compared. Product and purchasing information was checked on 13 October 2026 using first-party documentation; this is a buying guide, not a hands-on accuracy benchmark.
Six AI Search visibility data providers compared
Start with the report's required records: trends and competitor metrics, exact cited pages, or the underlying AI answers. Those needs can lead to different subscriptions even within the same platform.
Native reports, exports, connectors, REST APIs or MCP?
Native reports: keep the analysis in the platform
Native reporting works when the platform already provides the views your team needs and a useful way to share them. It is often the easiest option for inspecting answers, following competitor trends and discussing findings with colleagues. You do not need an external dashboard simply because the vendor offers an API.
Exports: prepare periodic reports with the records you need
An export suits a monthly client report, a one-off analysis or a presentation assembled by an analyst. Check what the file contains. A PDF chart, a CSV of daily scores and an export of individual answers are different deliverables.
A manual download does not establish scheduled extraction. If someone must repeatedly copy files into the same report, a connector or API may be worth adding. For less frequent work, that integration can be unnecessary.
Reporting connectors: use an existing dashboard
A Looker Studio connector brings supported fields into charts and tables without your team building the data connection from scratch. It can place AI Search trends and competitor comparisons alongside existing marketing reporting, with familiar filters and sharing.
The useful question is whether the connector supplies the fields your dashboard needs. It may expose cited URLs and country dimensions while leaving out full answer text. A vendor-maintained connector, a third-party connector and a custom dashboard fed through an API also have different purchasing and maintenance requirements.
REST APIs: retrieve structured records for your own reporting
A REST API lets your analytics team retrieve data into a warehouse, proprietary dashboard or reporting application. It is useful when you need custom transformations, a combined dataset across brands, or answer records that a connector does not expose.
In this article, an analytics API retrieves observations and metrics the visibility provider has already collected. A model API or collection service generates new answers that you would then need to analyze. For that separate distinction, see API vs UI data collection in AI visibility tools.
MCP: ask an AI assistant to investigate your analytics
An MCP connection gives a compatible assistant, such as Claude or ChatGPT, tools for retrieving available analytics from your provider. Instead of downloading a spreadsheet first, a marketer can ask for a market comparison, investigate competitor visibility or request a weekly performance summary.
The assistant can retrieve data, choose relevant views and explain the findings. Its written explanation is an interpretation of the records, so useful outputs should identify the questions, engines, markets and dates they cover. A summary alone is less helpful when you need to inspect the answers behind a change.
MCP is especially useful for follow-up questions that were not anticipated in a fixed dashboard. A connector is often better for a stable report read by many stakeholders; an API is better for repeatable structured extraction. These approaches can work together. Scheduling and distributing a weekly summary requires a host or automation workflow with those capabilities, not just an MCP connection.
Provider profiles: what data can you use outside the dashboard?
Superlines: tracked-answer analytics across brands and markets
Superlines is an AI Search analytics platform for enterprise marketing teams and agencies. Its REST API documentation covers aggregate visibility and Share of Voice, brand and competitor mentions, sentiment, domain- and URL-level citations, prompts and individual responses. Reports can be segmented by engine, country, prompt, label and date where the selected report supports those dimensions.
The distinction between a score and its evidence is explicit: response records contain the originating prompt, engine, date, detected brands and citation URLs. Full answer text is an optional field that must be requested. This makes the API useful for both recurring reports and investigations into what an AI answer actually said.
For dashboard reporting, the Looker Studio connector documents Brand Visibility, Share of Voice, Citation Rate, mentions and sentiment; engine, date, prompt, label, country and language dimensions; competitor metrics; and cited domains, URLs and counts. It does not list full answer text among its connector fields. Superlines customers use this integration for reporting.
The MCP server supports analytics, weekly trends, competitor comparisons and source investigation through compatible assistants. Paid plans include MCP; Starter and Pro use OAuth, while API keys are available on Growth and Enterprise. See the MCP/API product page for the reporting routes.
What to buy: Monthly brand pricing lists Starter at €79 for 50 prompts, Pro at €199 for 150, and Growth at €379 for 300, each with three selected engines. CSV, Excel and JSON exports begin on Pro; REST API and Looker Studio begin on Growth. History is one month on Starter, two on Pro, six on Growth and all-time on custom Enterprise. Agency Growth is also €379/month, with 20 client brands rather than the brand plan's seven. For larger historical extraction, public documentation does not establish a complete retrieval allowance; include that requirement in the package discussion.
Profound: Enterprise reporting with aggregate and raw-answer access
Profound's reporting APIs cover visibility, Share of Voice, position, sentiment and citations. Its Answer Engine Insights examples show comparisons by company, model, region, topic and prompt. The separate Answers API returns individual observations with full response text, prompt, date, model, region, mentions and citation URLs. Citation reports also support page-level analysis, so buyers can investigate specific pages rather than only a domain score.
Profound's MCP analytics tools expose visibility, sentiment, cited domains and pages, and raw prompt answers. The assistant can request a period and narrow relevant reports by models, regions, topics or brands. This is useful for teams that want to move from an executive trend to the answers behind it in one conversation.
What to buy: Current brand pricing lists a free trial and custom Enterprise. Enterprise lists CSV/JSON exports, API access and all-time history; the trial excludes API and exports. The MCP overview explicitly places MCP on Enterprise. The expected spend therefore requires an Enterprise quote, rather than treating the trial as an API evaluation package.
Profound also lets assistants run Agents. That is a separate purchasing distinction: direct API data retrieval does not itself consume Agent credits, while MCP-triggered Agent executions do. Buyers wanting only visibility reports should separate those retrieval requirements from any Agent workflows. For Looker Studio, specify whether the proposed connection is native, third-party or a custom API implementation; the sources reviewed here do not establish a native connector entitlement.
Semrush: existing suite reporting, with AI visibility API access through sales
Semrush is relevant to teams already using its marketing and SEO reports. Its AI Visibility Toolkit provides brand and competitor reporting, while My Reports guidance describes combining AI visibility widgets with other marketing data. That native route may meet the need without adding an external connection.
Semrush confirms API access to AI visibility data, including citations, mentions and platform-level insights, and directs interested buyers to sales. Its AI Visibility pricing page places custom integrations and API within its custom enterprise offer. Public descriptions do not establish an equivalent self-service allowance for downloading every underlying answer or citation record.
The public Semrush MCP should be evaluated separately. Semrush's own AI brand visibility capability guidance says the tested public MCP exposes AI-channel traffic estimates and organic/search context, but not AI prompt responses, brand mentions, citations, sentiment or AI Share of Voice. An assistant can use those traffic and search datasets, or analyze a supplied visibility export, but that is different from retrieving the Toolkit's answer analytics through MCP.
What to buy: The current page displays AI Visibility Base at $99/month per domain, billed annually, with 25 tracked custom prompts. That is a monitoring price, not a verified AI visibility API price. External API access requires a sales discussion covering the specific records, engines, markets and history needed. General Semrush API, MCP and Looker Studio access should not be taken as proof that those routes include the same AI Visibility reports.
Ahrefs Brand Radar: distinguish broad-index research from custom tracking
Ahrefs Brand Radar combines a broad AI-answer index with custom-prompt tracking. The index helps research a category without first building a monitoring cohort; custom tracking follows questions your team chooses. Keep those datasets identifiable in external reports because their question coverage and refresh schedules differ.
The AI Responses reference documents full response text, source links with URLs, question, engine, country and dates, with brand and competitor parameters. It supports Ahrefs index data, custom data or both. Custom-data requests require a saved report identifier. Other Brand Radar endpoints provide aggregate metrics, history and cited-page/domain reporting through API and MCP.
What to buy: Brand Radar pricing lists index access at $199/month per platform or $699/month for all platforms. Custom tracking is a separate check-based purchase or an included allowance on qualifying subscriptions. General API/MCP access is available on Lite and higher, with 200,000 monthly units and a 100-row request limit on Lite; higher plans increase those allowances.
The AI Responses endpoint makes an important cost distinction: requests returning only custom-prompt data consume no API units. Requests involving Ahrefs index data use standard API-unit pricing, with full response text and links each carrying a field cost. Free retrieval units do not make collecting custom observations free.
The Looker Studio documentation lists a Brand Radar connector and requires Advanced or higher. Teams already buying Ahrefs should therefore price the needed dataset and reporting route separately. Index history predates your subscription where available; custom history starts when those prompts were added.
Peec AI: connector reporting and conversational answer inspection
Peec's API provides brand, domain and URL reports plus individual chats. The chat detail reference includes the user prompt, assistant response, mentioned brands and source URLs. Those records support a deeper investigation than an aggregate visibility trend.
Peec's MCP tools include brand and source reports, chat lists and full chat inspection. Reports support date, model, brand and other relevant filters. MCP is included on paid plans, making conversational analytics a distinct option from buying direct REST access.
The Looker Studio connector documents brand, country, date, model/channel, prompt, source domain/URL, tag, topic and project dimensions. Metrics include visibility, sentiment, position, Share of Voice and separate citation, retrieval and usage measures. For source reporting, use the measure matching your question rather than treating every retrieved URL as a visible citation.
What to buy: Brand pricing places Looker Studio on Advanced, with 350 prompts and three selected models, and API access on custom Enterprise. The connector authenticates with an API key, but that does not establish unrestricted REST access on Advanced. Current numerical pricing should be checked for the selected brand configuration rather than inferred from a historical rate card.
Agency terms differ. Peec's agency page advertises API, MCP, Looker Studio and CSV across agency tiers, including Essential, whereas the API introduction describes access as Enterprise-only. Agencies should have the relevant API entitlement stated in their offer. The API documentation lists 200 requests per minute per project; history and bulk-export needs should also be matched to the chosen package.
Searchable: reporting APIs and MCP, with plan differences to resolve
Searchable documents visibility, Share of Voice, sentiment, competitor and topic reporting through its REST API and MCP server. MCP tools cover source discovery, source detail, prompts and raw AI answers. The answer tool can return full text in detailed mode, alongside platform, date, brand/competitor mentions and source URLs.
Source interpretation matters here: the answer documentation distinguishes URLs consulted for generation from inline citations. A report about visibly cited pages should preserve that distinction instead of relabeling every source URL as a citation. Date, platform, topic and location filtering are documented, but the available combinations differ by report. For a market-specific dashboard, choose a view that actually supports the required market filter.
What to buy: The current pricing page lists Pro at $125/month, Scale at $400 and Enterprise at $999, with respective allowances of 100, 500 and 1,250 tracked prompts per model. Additional model selections can affect the price. MCP and data exports are listed across paid tiers. The detailed table places REST API access on Scale and above, while the API documentation says paid-plan access more generally. Budget Scale for a direct API workflow unless Searchable confirms Pro eligibility.
Looker Studio is advertised on Scale and Enterprise plan cards, but the detailed table does not consistently match the Enterprise card. Confirm the connector's eligibility and fields in the selected offer before relying on it for a recurring report. The enterprise page describes tailored dashboards and integrations, which should not be assumed to mean a ready-made connector with full answer access.
Pro lists 12 months of data retention; Scale and Enterprise list unlimited retention. Retention is distinct from the window supported by an individual report. The REST documentation lists 600 requests per minute per API key, shared with the Looker Studio connector, while MCP uses separate concurrency limits.
Three practical reporting workflows
1. An enterprise Looker Studio dashboard
Imagine a marketing team maintaining a recurring dashboard for two priority markets. It needs Brand Visibility and Citation Rate trends, competitor comparisons, engine/country breakdowns and a table of recurring cited pages. A native connector can supply the supported fields without a bespoke data pipeline.
Superlines documents these metrics and dimensions in its Looker Studio integration. The team could put AI Search reporting alongside its GA4 and Search Console views while keeping answer visibility, website referrals and organic search performance as separate measures.
Move to REST API retrieval when the dashboard needs records the connector does not expose, custom transformations or warehouse storage. For example, a cited-page table can live in the dashboard while the full answer text is retrieved separately for investigation. The existence of a connector does not require every analysis to happen inside it.
2. An MCP-assisted market and competitor investigation
A marketer asks a connected assistant: “How has our AI Search visibility changed in Finland and Sweden over the last four weeks?” A useful follow-up is: “Which competitors gained visibility over the same period, and which cited sources should we investigate?”
The assistant retrieves the provider's available analytics, then explains the comparison and links it to relevant answer or source records. The analysis should use equivalent question sets, engines and dates where possible, with localized questions for each market. A headline country comparison is less useful if one market tracks a substantially different topic mix.
The same conversation can produce a weekly performance summary for review. The MCP connection supplies the data access; scheduling or distributing that summary requires a separate workflow. For the measurement setup behind those comparisons, see our generative search visibility measurement framework.
3. Answer-level evidence for a citation investigation
A competitor appears repeatedly in answers on an important buying topic. The marketing team needs to inspect the originating question, full AI answer, engine, market, date and exact cited URLs. A trend line tells the team where to look, but cannot explain what the competitor's cited page contributed.
Direct API retrieval is useful when those records are not available through the connector. MCP can also help when the provider exposes answer and source tools, and native answer inspection may be enough for a small investigation. Read the actual answer and cited page before choosing a content or PR action; recurrence alone does not establish why that source was selected.
For a monthly agency presentation, a suitable export may be sufficient. The report needs a clear explanation and relevant evidence, not necessarily a new integration. See how agencies use AI Search data in pitches and QBRs for applying those findings in client discussions.
What AI Search data access actually costs
Price the reporting requirement, not the cheapest monitoring tier. The relevant spend may be a higher subscription for a connector, a separate index subscription, or a custom package for answer-level API access.
Three differences have the largest practical effect:
- Monitoring versus retrieval: A tracked prompt, an AI answer and an API request are different units. More engines, markets or collection runs can increase observations even if the reporting frequency stays unchanged. Retrieval allowances determine how much of that data your report can pull.
- Access route and history: MCP may be included before REST or Looker Studio access. A connector's eligibility does not establish complete raw-answer access, and longer history can require a higher plan. Price the route and historical period the team actually needs.
- Retrieval versus Agent work: Retrieving analytics and running an Agent can have different costs. Profound explicitly separates direct API retrieval from credit-consuming Agent runs. Ahrefs separately distinguishes custom-only retrieval from unit-consuming index access.
For a dashboard refreshed weekly, request an allowance that covers the required brands, markets, records and history, including any repeated pulls. A per-minute rate limit describes request speed; it is not the same as a monthly allowance or retention period. Include implementation effort when comparing a native connector with a custom API workflow.
These costs should be assessed in each provider's own units. A price per tracked prompt is not a defensible substitute for the cost of comparable records across different engines and datasets.
Choose whether to keep, connect, upgrade or replace
Keep native reporting when it already answers the question and stakeholders can use the shared output. Use exports for periodic work that does not justify automation. Add a connector when the same supported fields belong in an established dashboard, and use REST when the requirement is structured extraction or custom reporting.
Choose MCP when your team benefits from asking follow-up questions through an assistant. It can make existing analytics easier to investigate, but should be evaluated on the records and filters available, not the presence of an MCP logo.
Before changing platforms, test whether the current provider can supply the report through a different route or subscription. Replace it when the required answers, citation URLs, markets or historical records are unavailable on a suitable offer. For broader platform selection, use our AI visibility tools comparison or GEO analytics tools guide.
The strongest choice is the one that supplies the evidence your team needs in a workflow it will actually use.
Frequently asked questions
Can I keep a competitor's visibility data after changing providers?
An available export or API may let you retain historical records, but check the contract's permitted use and the fields included before leaving. Export the prompt wording, dates, engines and markets alongside the metrics so the retained data remains interpretable.
Can I combine visibility scores from two providers in one report?
You can display them together, but preserve each provider's definition and dataset. Matching names such as “visibility” or “citation rate” do not establish equivalent calculations. Do not average unlike scores into a single brand result.
Does API access include historical data from before we started tracking?
Only if the product supplies an existing dataset covering that period. Ahrefs' research index and a customer's newly added custom prompts illustrate the difference. An API can retrieve available history; it does not create observations that were never collected.