Ahrefs Brand Radar API Endpoints: What January 2026 Added

Ahrefs released 17 product updates for January 2026, but two changes matter most for AI-search reporting: new Brand Radar endpoints for cited pages and cited domains, plus page-type and category data in Site Explorer backlink reports. This guide explains the Ahrefs Brand Radar API endpoints release in practical terms, so SEOs can turn AI-response, citation, and link-context data into reports and prioritised work instead of manual exports. (ahrefs.com)

This is specifically a January 2026 release explainer. It does not treat later April 2026 additions—such as entity support, brand-variation and brand-website inputs, Grok coverage, and revised citation-report views—as if they were part of the original January launch. That distinction matters when documenting an API integration, comparing historical data, or deciding what an older workflow could actually do at the time. (ahrefs.com)

What Ahrefs released in January 2026

The January update, published by Ahrefs on February 6, 2026, introduced two closely related Brand Radar API data sets: cited pages and cited domains. Ahrefs said these endpoints return pages and domains cited in AI responses, how often each appeared, and estimated monthly search demand associated with the underlying mentions. (ahrefs.com)

The release also completed the remaining overview-style API coverage for AI Share of Voice distribution and historical trends. In other words, a team could move beyond a point-in-time list of AI citations and programmatically track whether brand visibility was moving over time. (ahrefs.com)

The January package included several non-API improvements too:

• Standalone custom prompt packages for focused AI-monitoring work.
• A Search Demand view combining total search volume and query counts.
• Search-volume-by-location reporting and a Brand name only mode.
• Site Explorer backlink page types and categories.
• A crawl-pages chart that counted only live 200 OK URLs.

Those adjacent improvements are important because they make the API data easier to interpret. A cited domain list alone does not explain whether demand is branded, whether a source is a direct competitor, or whether a backlink comes from an editorial article versus an unrelated template-driven page. (ahrefs.com)

Ahrefs Brand Radar API endpoints released for AI visibility

The January API expansion was about turning Brand Radar into a usable data source for reporting systems, dashboards, and repeatable agency processes. Ahrefs’ current Brand Radar developer reference lists AI Responses, Cited Pages, Cited Domains, overview endpoints, and overview-history endpoints under the Brand Radar API. (docs.ahrefs.com)

Cited Pages: identify the exact URLs AI systems reference

The cited-pages endpoint is the page-level view. It is the most actionable endpoint when the question is: Which content URLs appear as sources in AI answers?

A practical workflow might group cited pages into three buckets:

1. Pages on the audited site — content that is already earning citations and may deserve protection, internal links, updates, or conversion improvements.
2. Competitor pages — rival URLs repeatedly used in responses for the same topic set.
3. Third-party publisher pages — editorial, directory, review, or reference pages that influence the answer landscape.

For example, an agency auditing a B2B software client may find that a competitor’s integration guide appears in 35 AI responses while the client’s feature page appears in two. The appropriate action is not automatically “write more blog posts.” It may be to improve the feature page’s entity clarity, supporting documentation, comparative evidence, crawlability, and internal linking.

The distinction between a cited page and a cited domain is central to useful analysis. A domain can be visible because one strong URL is repeatedly cited, while another domain may earn broad citations across dozens of pages. The first situation suggests a page-level content opportunity; the second may indicate a wider topical-authority gap. For a deeper framework, see Cited Domains vs Cited Pages: Ahrefs Brand Radar vs Audra Workflows.

Cited Domains: measure source-level influence

The cited-domains endpoint aggregates AI citations at the domain level. Ahrefs documents that the endpoint can return domains cited in AI responses along with counts relating to mentions, pages, responses, and volume. (docs.ahrefs.com)

This supports a different set of questions:

• Which publishers, brands, or marketplaces dominate citations in a category?
• Are AI systems citing the audited brand’s domain at all?
• Is a competitor’s visibility concentrated in its own site or amplified by third-party sources?
• Which domains should be reviewed for digital PR, partnership, listing, or content-gap opportunities?

Domain-level analysis should not be mistaken for a ranking report. A cited domain is evidence that a source appeared in an AI response measured by the selected report, date, prompts, market, and AI data source. It does not prove that every URL on that domain is trusted equally, or that a citation drove a visit or conversion.

AI Share of Voice distribution and history

January also brought overview endpoints for AI Share of Voice distribution and historical trends. That means a reporting workflow can record a starting point, compare brands and competitors, and show directional movement rather than pasting a one-off screenshot into a deck. (ahrefs.com)

This is especially useful for monthly client reporting. A chart can show whether visibility changed after a technical deployment, content refresh, PR campaign, product release, or major shift in the tracked prompt set. It still needs annotation: historical movement is a signal to investigate, not proof that a specific SEO change caused an AI citation increase.

What the API data can and cannot tell a team

Brand Radar is an AI-visibility measurement product, not a complete website audit. Ahrefs currently describes Brand Radar as tracking hundreds of millions of search-backed prompts across multiple AI platforms, while custom prompts allow teams to monitor selected questions on defined schedules. (help.ahrefs.com)

That gives the API strong uses in discovery and reporting, but it does not replace page diagnostics.

Current documentation shows that API requests may be scoped by report, prompts, countries, data sources, and filter expressions. It also notes that custom-prompt-only requests can have different API-unit treatment than requests using Ahrefs’ broader prompt data. Teams should check their own plan and API documentation before estimating reporting costs or polling frequency. (docs.ahrefs.com)

How to use cited pages and domains in an agency workflow

A good implementation begins with a reporting question, not an endpoint. “Pull all citations” produces a large table; “explain why this client is absent from AI answers for ten commercial questions” produces an audit plan.

A repeatable five-step process

1. Define a stable prompt set. Separate discovery prompts from the 20 to 100 questions that matter commercially. A prompt set should identify the market, country, language, audience, and AI platforms being monitored.
2. Pull cited domains first. This shows who owns the answer landscape: the client, direct competitors, publishers, marketplaces, review sites, or forums.
3. Drill into cited pages. Export or retrieve the URLs behind priority domains. Group pages by intent, format, subfolder, and content type.
4. Review the actual AI responses. Citation count without response context can mislead. Determine whether the cited URL supports a recommendation, definition, comparison, statistic, or a tangential claim.
5. Pair the finding with a site audit. Check whether the client’s relevant pages are indexable, internally linked, fast enough, accessible, canonicalised correctly, and substantively able to answer the question.

The final step is where many AI-visibility dashboards stop too early. A brand can lack citations because it has no credible page for the query, but it can also lack citations because the right page is noindexed, blocked, slow, duplicated, thin, or poorly linked. AI-response monitoring identifies the opportunity; a technical and content audit identifies what to fix.

For teams monitoring Google’s changing AI surfaces, the handoff between AI Overviews and AI Mode also needs to be treated as a reporting variable rather than a single blended channel. Google AI Overviews to AI Mode: What the New Handoff Means for SEO explains why reporting definitions should be explicit.

Page Types in Ahrefs Site Explorer: why link context matters

January 2026 also added page type and page category fields to Site Explorer backlink reports. Ahrefs defines page type as the format of a referring page—examples include articles, listicles, and tools—while category represents the referring page’s topic or business niche. The fields are available in reports, exports, the API, and Ahrefs MCP. (ahrefs.com)

This is a meaningful improvement over reviewing links only by DR, traffic estimate, anchor text, or referring-domain count.

Page type answers “what format linked?”

A backlink from a comparison article, a how-to guide, a calculator, and a directory listing can all have different strategic value. Page Types lets an analyst segment the backlink profile by format instead of manually classifying every URL.

For example, a SaaS brand with 500 new referring pages may discover that 350 came from listicles and coupon pages, while only 20 came from relevant editorial articles. The raw link count looks healthy; the page-type mix tells a more useful story about whether the brand is earning references likely to strengthen topical credibility.

Page category answers “is the context relevant?”

Categories add another layer: topic and niche relevance. A fitness-site article linking to accounting software is not necessarily worthless, but it does not carry the same contextual meaning as a finance publication or accounting-resource link.

The January release did not claim that page type or category is a direct Google ranking factor. It made backlink reporting more descriptive and easier to segment. Teams should use these labels as prioritisation aids, then manually review high-impact links before making quality judgments.

Brand variations, entities, tags, and domains: the timeline matters

The January 2026 release should not be credited with every filtering capability now visible in the Brand Radar API.

January 2026: cited-pages and cited-domains endpoints, plus AI Share of Voice overview and history coverage, were the major Brand Radar API additions. (ahrefs.com)

April 2026: Ahrefs added entity support to Brand Radar API endpoints. Ahrefs said POST versions of existing GET endpoints could accept JSON objects for more complex entity inputs, including brand variations and brand websites, aligning API capability with the UI. The April update also added Grok to Brand Radar and changed the cited-pages reporting experience. (ahrefs.com)

July 2026: Ahrefs added a brand_filter to selected POST endpoints for conditions such as pages found but not cited, and later added Claude as a data source across Brand Radar endpoints. (docs.ahrefs.com)

Tags are also relevant, but their availability depends on the endpoint. For example, Ahrefs’ citations-overview documentation describes a tags_filter that requires a report ID and filters prompt tags. That is not the same as saying all January endpoints supported all tag, entity, domain, and variation filters from day one. (docs.ahrefs.com)

This timeline is important for data teams. If an integration started in February 2026, it may need a versioned changelog, a schema review, and revised queries before it can reliably use later entity-based workflows.

Standalone custom prompts and Search Demand improvements

The January update made custom prompt packages available without a full Brand Radar subscription. Ahrefs positioned this as a lower-friction option for teams that only need to monitor defined questions rather than a broad AI-visibility dataset. (ahrefs.com)

That fits several situations:

• A local business tracking a small number of service and location questions.
• A B2B company monitoring named competitive comparisons.
• An agency running a short-term launch, migration, or reputation-monitoring project.
• A product marketing team checking whether AI answers reflect a newly released capability.

The Search Demand tab changes are equally practical. Combining search-volume and query views, adding location-level demand, and providing a Brand name only mode helps separate branded interest from category demand. (ahrefs.com)

For instance, if a company name overlaps with a common category term, total demand can overstate genuine brand demand. A brand-name-only view creates a cleaner baseline for measuring whether awareness is actually growing. That kind of segmentation should be documented in client reports so month-to-month comparisons use the same definition.

Where a local-first audit workflow is more practical

An Ahrefs API workflow is valuable when a team needs recurring monitoring at scale, system integrations, or broad competitive intelligence. It can be less practical when the immediate task is a one-off or client-ready website audit covering technical SEO, performance, accessibility, and on-page issues alongside AI visibility.

That is where a local-first desktop workflow can be simpler. Audra is designed to audit sites locally on macOS and Windows, checking AI answer-engine visibility alongside SEO, performance, accessibility, best practices, and links without requiring a recurring SaaS subscription. Rather than sending a client from one dashboard to another, an agency can connect the visibility question to page-level findings in one report.

A useful division of labour is:

• Use Brand Radar API data when ongoing AI-response monitoring and competitor citation intelligence justify the API investment.
• Use a crawl-based local audit when a client needs concrete page fixes, repeatable deliverables, and coverage across technical and experience signals.
• Combine both when the AI visibility report identifies the where and the audit establishes the why and what next.

This distinction also applies to performance data. A sampled or limited view can miss affected URLs on a large site; Semrush Site Audit Core Web Vitals vs Audra: What Page Sampling Misses outlines why page-level coverage changes the quality of remediation work.

Implementation checklist for SEOs, agencies, and site owners

The most useful outcome of the January 2026 Ahrefs release is not a larger dashboard. It is a more disciplined workflow connecting AI citations to real site improvements.

Before building an integration or report, teams should confirm the following:

• Reporting scope: AI platforms, countries, languages, competitors, and prompt types.
• Metric definitions: whether the report measures citations, mentions, responses, Share of Voice, search demand, or a mix.
• Time period: a fixed snapshot date plus a consistent comparison window.
• Entity setup: whether later entity, brand-variation, and brand-website functions are required.
• Source evaluation: page-level citation review rather than only domain-level totals.
• On-site validation: indexability, content quality, internal links, accessibility, performance, and technical SEO checks for affected pages.
• Client deliverable: a short list of actions, owners, evidence, and expected outcome—not just exported data.

Agencies benefit most because the endpoints can reduce repetitive reporting work across accounts. In-house SEO and product marketing teams benefit when they need visibility trends connected to launches or reputation questions. Site owners with a small set of business-critical prompts may get more value from focused custom tracking and a practical audit than from a complex API build.

FAQ

What new Brand Radar API endpoints were released in January 2026?

Ahrefs’ January 2026 product update added endpoints for cited pages and cited domains, allowing users to retrieve sources appearing in AI responses, their appearance frequency, and associated estimated monthly search demand. The release also completed overview-based API coverage for AI Share of Voice distribution and history. (ahrefs.com)

How can Brand Radar API endpoints monitor AI responses and cited pages?

A team can retrieve AI responses for a defined report or prompt set, identify cited domains to understand source-level visibility, then inspect cited pages to find the URLs behind that visibility. The workflow is most useful when filtered by platform, country, date, and prompt group, then paired with manual response review and a crawl-based site audit. (docs.ahrefs.com)

What are Page Types in Ahrefs Site Explorer, and how do they affect site analysis?

Page Types classify the format of referring pages in backlink reports, such as articles, listicles, or tools. Page Categories describe topical or niche context. They do not establish a direct ranking factor, but they help analysts segment link profiles and distinguish relevant editorial coverage from less relevant link sources. (ahrefs.com)

Can Brand Radar API filter results by tags, entities, domains, or brand variations?

It depends on the endpoint and release date. Tag filtering is documented for some overview requests and requires a report ID. Entity support, including brand variations and brand websites through JSON-based POST requests, arrived later in April 2026 rather than in the January release. Cited-domain and cited-page data supports source-level analysis, while later filters added further control. (docs.ahrefs.com)

Who benefits most from these Ahrefs API and Site Explorer features?

Agencies and enterprise SEO teams benefit most when they need automated, recurring AI-visibility reporting across brands or competitors. In-house marketers benefit when they must connect AI-answer trends to launches and category demand. Smaller site owners may prefer a focused custom-prompt plan and a local technical audit when the priority is a short, actionable list of page fixes rather than a full API integration. (ahrefs.com)