Ahrefs Academy: AEO course

Executive Overview

Ahrefs Academy: AEO course is a practitioner-oriented online training program produced by Ahrefs, the Singapore-based search marketing software company. It is an educational resource rather than a separate company, managed GEO service, or standalone measurement platform. The course introduces answer engine optimization, explains how AI-generated discovery differs from conventional search, and shows marketers how to research brand visibility, citations, competitors, topics, and content opportunities. Its practical context includes Ahrefs tools such as Brand Radar and the company’s broader SEO research platform.

The primary audience is in-house SEO, content, brand, communications, and digital marketing teams, along with agencies advising clients in the technology sector and other industries. The course is publicly available without a separate tuition charge, making it part of Ahrefs’ education-led acquisition, activation, and customer-retention model. Its differentiation comes from connecting instruction to Ahrefs’ web index, search data, AI-response datasets, and ongoing research. In the provider landscape, it is best understood as vendor-led training that helps users interpret and act on AI visibility data. It does not itself implement campaigns, control answer engines, or guarantee mentions, recommendations, citations, traffic, or conversions.

Ahrefs Academy: AEO course at a Glance

Logo
Name	Ahrefs Academy: AEO course
Segment	Podcasts, video channels, and training
Headquarters	Singapore (Ahrefs headquarters)
Organization type	Online AEO training course within Ahrefs Academy
Official website	Ahrefs Academy: AEO course

1. What Is the GEO Strategy of Ahrefs Academy: AEO course?

Winning Aspiration

The course addresses a practical knowledge gap: marketers accustomed to conventional search rankings need a framework for understanding discovery through AI answers. Its implied aspiration is to make answer engine optimization a measurable extension of SEO and brand marketing rather than an isolated technical specialty. The value proposition is education—helping learners identify where a brand appears, which sources influence answers, how competitors gain visibility, and which actions are plausible responses.

Where It Plays

The course plays in self-directed professional education for SEO practitioners, content teams, digital marketers, brand managers, communications professionals, and agencies. It is globally accessible online and relevant to organizations that depend on web discovery. Its scope covers research, measurement, source analysis, and content planning across AI-assisted search and answer interfaces. It is not a substitute for enterprise implementation support, digital PR execution, or a full change-management program.

How It Tries to Win

Its main differentiator is the connection between instruction and Ahrefs’ own datasets and workflows. Rather than discussing GEO only as a theory, the course can show how to examine prompts, mentions, cited pages, competing entities, and topic-level visibility using Ahrefs products. The curriculum also benefits from Ahrefs’ established audience among SEO professionals and its ability to publish studies based on search, web, and AI-response data.

Required Capabilities

Execution requires instructors who understand retrieval-based AI systems and SEO, reliable product demonstrations, maintained course material, large-scale web and answer-engine data, and a clear distinction between observed results and recommendations. Ahrefs also needs to update examples as AI interfaces, source-selection behavior, and its own Brand Radar coverage change.

Management Systems

Relevant disciplines include curriculum governance, product-data quality, repeatable research methods, platform monitoring, security for customer accounts, and feedback from learners. Public information does not establish a separate management system for the course. It appears to operate within Ahrefs’ broader academy, content, product marketing, and product education functions.

2. What Are the Strategic Priorities of Ahrefs Academy: AEO course?

The following themes are reasonable interpretations of the course, Ahrefs’ product development, and its public research. They should not be read as a formal list of corporate priorities.

• Extend SEO education into AI discovery. The course helps Ahrefs remain relevant as search behavior expands from lists of links to synthesized answers, conversational research, and AI-assisted recommendations.
• Connect learning with AI-visibility measurement. Ahrefs has expanded from traditional keyword, backlink, and site analysis into brand and citation analysis through Brand Radar. The course gives users a framework for applying those capabilities.
• Turn proprietary data into practical instruction. Ahrefs publishes research on AI citations, brand mentions, search overlap, and referral behavior. Converting that research into lessons can make the findings more usable while demonstrating the value of the underlying datasets.
• Bridge measurement and action. Visibility dashboards are more useful when teams can translate findings into source analysis, content decisions, technical work, brand building, or digital PR. The curriculum helps create that bridge, although implementation remains with the learner or its advisers.
• Support global self-service adoption. A publicly accessible course can educate many users without a consulting-intensive delivery model. It also helps agencies and internal champions train colleagues around a shared vocabulary.
• Maintain credibility amid volatile platform behavior. Durable education requires Ahrefs to update demonstrations and avoid treating temporary correlations or interface behavior as permanent ranking rules.

3. What Is the Business Model of Ahrefs Academy: AEO course?

The AEO course is an educational offering embedded in Ahrefs’ software business rather than an independently priced training company. Customers do not appear to buy the course as a separate consulting project or managed service. Access to the learning material is offered without a standalone course fee, while some demonstrated research and measurement workflows may require access to relevant Ahrefs products or plan capabilities.

Ahrefs earns primarily through subscriptions to its search marketing and analytics software. The course supports that model in several ways:

• Acquisition: marketers searching for AEO guidance can discover Ahrefs through useful training before becoming software customers.
• Activation: existing users learn how to apply newer capabilities such as AI visibility and citation research.
• Retention: training can embed Ahrefs data in recurring research, reporting, and content-planning routines.
• Category development: education helps establish common concepts and metrics for a software category that remains unfamiliar to many buyers.

The economic buyer for Ahrefs software may be an SEO leader, marketing executive, agency owner, or procurement function, while the course learner may be an individual practitioner. Value is created when education shortens the time required to interpret data and make decisions. Repeat use comes from changing AI answers, new content, competitor activity, and the need to report trends over time.

Switching costs arise from accumulated reports, saved projects, historical comparisons, learned workflows, and integration of Ahrefs data into team processes. These are product-level switching costs, not restrictions created by the course. Pricing and feature access can change by plan; the durable point is that free education complements a subscription software model.

4. What GEO Products and Services Does Ahrefs Academy: AEO course Offer?

Core Training

The core offering is a structured sequence of video lessons explaining answer engine optimization and practical AI-visibility research. The curriculum introduces how answer engines discover and assemble information, how brands can examine their presence, and how findings can inform content and off-site visibility work. Its short-form, self-directed format makes it suitable for individual learning or as shared training for a marketing team.

Research and Measurement Workflows

The course connects AEO concepts to workflows for analyzing brand mentions, citations, competing brands, topics, prompts, and source domains. Ahrefs’ associated Brand Radar product is the principal AI-visibility context. It is designed to help users search and monitor how brands, products, and competitors appear across supported AI-answer datasets. Depending on current product coverage, users can examine response-level evidence, cited URLs, changes over time, and differences among engines.

Adjacent Ahrefs Capabilities

The broader Ahrefs suite contributes supporting evidence. Site Explorer can help analyze pages and domains that earn links or visibility; Keywords Explorer can inform demand and topic research; Content Explorer can identify publishers and widely referenced content; site-audit capabilities can surface crawlability and technical issues. These products were not created solely for GEO, but they can support investigation and execution.

Research and Educational Content

Ahrefs also publishes studies, articles, videos, and product guidance concerning AI Overviews, answer engines, citations, mentions, and AI referral traffic. This material gives the course an evolving evidence base and provides follow-on learning beyond the formal lessons.

The course does not itself run prompt monitoring in the background, rewrite a customer’s website, conduct publisher outreach, or provide managed AEO campaigns. Those activities require Ahrefs software, internal teams, agencies, consultants, or other execution partners.

5. Which Information Routes and AI Platforms Does Ahrefs Academy: AEO course Address?

The course treats AI visibility as the result of several information routes rather than a single ranking position.

• Owned website content: answer engines may retrieve, cite, summarize, or otherwise use accessible pages. The course helps learners assess whether their content addresses relevant questions and provides information that can be understood and corroborated.
• Search indexes and live retrieval: many AI experiences use search or retrieval systems to gather current information. Conventional discoverability, crawlability, topical relevance, and search visibility may therefore affect the candidate sources available to an answer engine.
• Third-party publications: reviews, comparisons, news coverage, directories, expert articles, and other independent pages can shape which brands appear in category answers. Ahrefs can measure cited and mentioning domains; the course can guide source-gap analysis but does not secure publisher coverage.
• Brand and entity information: consistent descriptions, associations with a category, and corroboration across the web may help systems identify an organization and its attributes. Ahrefs research emphasizes web mentions, although a statistical association does not prove that mentions directly cause an answer.
• Citations: Brand Radar and related workflows can expose pages or domains cited in generated answers. A citation is evidence that a source was surfaced in a sampled response; it is not automatically an endorsement of the cited brand.
• Technical access: robots directives, page rendering, internal linking, and indexability can affect whether retrieval systems access content. Access is necessary in some workflows but does not guarantee selection, citation, or recommendation.

Ahrefs’ related AI-visibility materials cover major answer and AI-search experiences including ChatGPT, Google AI Overviews and AI Mode, Gemini, Perplexity, and Microsoft Copilot. The exact indexes, histories, countries, and collection methods available in Brand Radar can differ by platform and can change as Ahrefs updates the product.

These systems should not be treated as interchangeable. Some answers rely heavily on live web retrieval, some blend model knowledge with retrieved sources, and some vary by account, location, interface, or query mode. The course does not establish that optimizing one source route will produce the same result on every platform. It also does not imply that website publication places content into a model’s training data.

6. How Does Ahrefs Academy: AEO course Measure GEO Performance?

The course teaches measurement rather than generating measurements independently. Its practical measurement layer is primarily Ahrefs Brand Radar and related Ahrefs datasets. Useful metrics and diagnostic views include:

• Brand presence and mentions: whether a selected brand appears in sampled AI answers and how often it appears within the available dataset.
• AI share of voice: a brand’s relative presence compared with named competitors for the topics or prompt set being examined.
• Citations: the URLs and domains cited by answer engines, including whether the customer’s own properties are used as sources.
• Source concentration: publishers and domains that repeatedly influence answers in a category.
• Competitive visibility: brands that appear where the customer does not, which can identify content, positioning, or third-party-source gaps.
• Topic and prompt performance: the questions or themes associated with a brand, product, or competitor.
• Historical movement: changes in observed presence, response patterns, and citations where the product retains comparable historical data.

Ahrefs’ approach combines a searchable database of collected AI responses with its established web and search datasets. This differs from tools that only rerun a small customer-entered prompt list. Broad datasets can improve category discovery and competitive research, while tracked prompts can provide a controlled monitoring set. Each has limitations: broad corpora may not match the customer’s real demand, and manually chosen prompts may reflect internal assumptions rather than user behavior.

Counts of mentions and citations are observed within the collected sample. Share-of-voice or visibility scores are calculated summaries, not universal measures of all AI use. Search volume, estimated traffic, or opportunity measures may be modeled from other datasets. Learners should record the engine, date, country, language, prompt wording, and collection method whenever those controls are available.

Favorability requires more than a presence count. A brand can be mentioned negatively, cited only as background, or omitted from the final recommendation. Referral traffic and conversions require analytics outside the answer dataset. Consequently, teams should combine answer visibility with site analytics, lead data, qualitative response review, and business outcomes rather than use one proprietary score as the sole KPI.

7. How Does Ahrefs Academy: AEO course Try to Improve AI Visibility or Favorability?

The course’s optimization logic starts with evidence: identify relevant answer topics, inspect which brands and sources appear, compare the customer’s coverage with competitors, and prioritize gaps. It does not offer a mechanism for directly editing an answer engine’s output.

Likely actions arising from the workflow include:

• Improve useful owned content. Teams can create or refresh pages that answer real questions clearly, provide verifiable detail, and cover missing subtopics. Comparison pages, product explanations, definitions, original data, and expert commentary can be useful when they genuinely satisfy the query.
• Strengthen technical discoverability. Crawlable pages, coherent internal links, descriptive titles, stable URLs, and appropriate structured data can help search and retrieval systems process content. Structured data clarifies information but does not guarantee an AI citation.
• Analyze cited-source gaps. If particular publishers repeatedly shape answers, communications or digital PR teams can evaluate legitimate opportunities for expert contribution, research promotion, product review, or correction of inaccurate information.
• Build corroborated brand presence. Consistent, accurate information across owned and credible third-party sources may make an entity easier to understand. The plausible mechanism is stronger retrieval and corroboration, not “training” a model to prefer the brand.
• Monitor and iterate. Teams can repeat measurements after content or outreach activity, while recognizing that simultaneous platform changes prevent simple before-and-after comparisons from proving causality.

Ahrefs’ public research provides a technical rationale for considering brand mentions, search visibility, links, and source authority together. Much of that evidence is correlational. Customer teams still need editorial judgment, communications capability, and implementation resources. The course can improve decision quality, but it cannot promise visibility, favorable portrayal, citations, recommendations, referral traffic, or revenue.

8. Who Uses Ahrefs Academy: AEO course?

The course is open to individual learners, but its most relevant organizational users are teams responsible for digital discovery and brand representation:

• SEO professionals extending keyword, ranking, backlink, and technical analysis into AI-generated answers.
• Content strategists and editors deciding which questions, formats, comparisons, and evidence deserve coverage.
• Brand and communications teams evaluating how an organization is described and which third-party sources influence that portrayal.
• Digital PR practitioners researching publishers and topics that appear in AI citations.
• Marketing analysts building dashboards that combine answer visibility, search performance, referral traffic, and conversions.
• Agencies and independent consultants developing AEO services, training account teams, or preparing client diagnostics.
• Marketing leaders seeking enough conceptual knowledge to assess tools, approve experiments, and set realistic expectations.

The training can serve companies of different sizes because access is self-directed. Smaller organizations may use it to create an initial measurement and content plan. Enterprises may use it as foundation training before establishing governance, regional prompt sets, reporting standards, and cross-functional ownership.

The subject matter is industry-neutral, but it is especially relevant where customers conduct extensive online research before purchasing: software, professional services, financial products, healthcare, education, ecommerce, travel, and complex business-to-business categories. Regulated industries require additional legal, medical, compliance, and reputation review that the general course does not replace.

9. Who Competes with Ahrefs Academy: AEO course?

• Semrush Academy: Semrush offers free training tied to its SEO and AI-visibility products. It is the closest structural comparison: both vendors use education to teach a developing category and increase software adoption. The practical difference is the underlying product ecosystem, datasets, interfaces, and methodology demonstrated in each curriculum.
• Moz Academy: Moz provides established SEO education and training on content, technical optimization, and search strategy. It competes for practitioner learning time and budget. Ahrefs’ AEO course is more directly framed around answer-engine visibility and connected to Ahrefs’ AI-response research.
• Yoast SEO Academy: Yoast emphasizes accessible SEO instruction and implementation within content and WordPress workflows. It is relevant for learners seeking hands-on publishing guidance. Its center of gravity is on-site optimization, whereas the Ahrefs course places greater emphasis on competitive datasets, citations, and cross-domain research.
• CXL: CXL sells advanced, instructor-led digital marketing education across content, analytics, experimentation, and AI-related subjects. It can offer broader strategic depth and multiple expert perspectives. Unlike Ahrefs Academy’s vendor-led model, CXL’s primary product is paid education rather than adoption of one SEO data platform.
• Blue Array Academy and specialist SEO training providers: Specialist academies can offer focused AI-search modules, cohort learning, certification, or instructor access. They may provide more direct feedback than a self-paced course, while lacking Ahrefs’ integrated web, search, and AI-response datasets.
• Agency and consultancy workshops: Customized AEO workshops compete as a substitute for public training. They can use a client’s products, markets, governance constraints, and analytics, and may include implementation planning. They are generally more resource-intensive and less scalable than a free self-directed course.

The competitive set therefore spans other SEO-suite academies, dedicated education businesses, specialist training providers, and customized advisory services. Software products compete directly with Brand Radar; training resources compete more directly with the course itself.

10. How Does Ahrefs Academy: AEO course Market and Sell Its GEO Offering?

The course uses an education-led and product-led go-to-market model. Its public landing page, searchable lessons, articles, videos, research studies, newsletters, and social distribution can attract practitioners already investigating AI search. The absence of a separate tuition decision reduces friction and lets a learner experience Ahrefs’ methodology before evaluating paid software.

Demonstrations create a natural route from a question—such as “Where is my brand cited?”—to an Ahrefs workflow that can answer it at scale. This makes the course both a standalone educational asset and a product-adoption channel. Research on AI citations, brand mentions, and referral behavior also supports thought leadership, while product tutorials help existing customers use newer capabilities.

The wider Ahrefs model includes self-service software adoption and sales support for larger organizational needs. Agencies are an important audience because one trained user can apply the methodology across multiple clients, although the course is not itself an agency partner program. Team sharing and internal training can generate bottom-up demand before an enterprise buyer evaluates data coverage, security, permissions, support, and commercial terms.

This model fits a fast-changing category: educational content can explain the problem, software can supply recurring data, and updates can be distributed as platform behavior evolves. Its risk is that training may be perceived as product marketing unless limitations and alternative interpretations are made explicit.

11. Where Does Ahrefs Academy: AEO course Operate?

The course is delivered online by Ahrefs, whose headquarters are in Singapore and whose workforce and customer base are internationally distributed. It is not tied to a classroom, local office network, or one national market. This gives agencies and in-house teams broad access wherever the website and related products are available.

The core public course is presented in English. Ahrefs’ wider search datasets cover many countries and languages, but geographic coverage in conventional search data should not be assumed to equal geographic coverage in every AI-response index. AI interfaces may return different answers according to location, language, personalization, account state, or product mode.

Multinational organizations can use the course’s framework across regions, but they need local prompt research and native-language review. Translating an English prompt list is not always sufficient because category vocabulary, publishers, competitors, regulations, and customer concerns vary by market. Teams should verify which country and language controls are available for each supported engine and document any gaps in regional sampling.

12. Who Owns and Operates Ahrefs Academy: AEO course?

The AEO course is produced and operated by Ahrefs rather than by a separate educational company. Ahrefs is an independent software business founded in 2010 by Dmitry Gerasimenko and headquartered in Singapore. The operating company is commonly identified as Ahrefs Pte. Ltd. Ahrefs publicly describes itself as a bootstrapped business.

Ahrefs Academy is a branded educational function within the wider organization. It supports product education and market development but is not presented as an independent subsidiary. The course title should therefore not be confused with a legal entity, an acquired AEO company, or a standalone software product.

In practical terms, several Ahrefs capabilities contribute to the offering: subject-matter experts and educators develop lessons; product teams maintain the demonstrated tools; data and infrastructure teams operate the web, search, and AI-response datasets; and marketing teams distribute the material. Public information does not provide a distinct organizational chart or separate ownership structure for the course.

This structure offers close access to product data and research, but it also creates a commercial perspective. Learners should distinguish general AEO principles from workflows designed around Ahrefs products and should test recommendations against their own analytics and other evidence.

13. How Does Ahrefs Academy: AEO course Operate and Integrate with Customer Workflows?

Delivery is primarily learner-led. A user watches the video material, applies the concepts to a brand or category, and uses available Ahrefs tools to investigate answers, mentions, citations, topics, and competitors. There is no indication that enrolling in the course initiates a vendor-managed implementation.

A typical operational workflow is:

1. Define the brands, products, competitors, markets, and business questions that matter.
2. Research category prompts and inspect the available AI-response data rather than relying only on an internally imagined prompt list.
3. Measure mentions, share of voice, citations, source domains, and response context across supported platforms.
4. Use Ahrefs’ web and search data to investigate cited pages, competing content, links, brand mentions, and topic demand.
5. Prioritize technical, editorial, brand, or digital PR actions according to expected business value and feasibility.
6. Repeat collection and combine visibility trends with web analytics, leads, and conversion evidence.

Relevant Ahrefs workflows can draw on verified properties and search-performance data from Google Search Console, along with Ahrefs’ own crawler, search databases, site-audit products, and web analytics capabilities. Data can also be exported for reporting and further analysis. API availability and specific endpoints depend on the Ahrefs product and plan; the course itself is not an integration layer.

Implementation is therefore mixed. Ahrefs supplies training, datasets, interfaces, and diagnostic recommendations. Customers or their agencies make content changes, adjust technical settings, conduct outreach, manage approvals, and connect findings to business reporting. Enterprises may need additional processes for access control, legal review, regional ownership, prompt governance, experiment design, and executive reporting.

14. What Evidence Supports Ahrefs Academy: AEO course, and What Are Its Limitations?

Company-Published Evidence

Ahrefs publishes product demonstrations, large-scale analyses, and practitioner studies concerning AI citations, web mentions, search rankings, and referral traffic. Its research has reported associations between brand visibility in AI answers and signals such as branded web mentions, search demand, links, and broader web presence. These studies are useful for generating hypotheses and show that Ahrefs can analyze sizable datasets. They do not by themselves demonstrate that taking one prescribed action will cause an answer engine to recommend a brand.

The course also provides evidence of a defined, practical curriculum: learners can see the methodology and reproduce parts of the workflow on their own organization. Public training, however, is evidence that a method can be taught—not proof of customer business impact.

Independent Evidence

Independent research into generative engine optimization has found that answer structure, quotations, statistics, authoritative sourcing, and other content characteristics can affect visibility in controlled experiments. Information-retrieval research also supports the importance of relevance, source selection, entity recognition, and corroboration. Results vary significantly by model, benchmark, query, and evaluation method, so they validate the general rationale more than any specific Ahrefs score or workflow.

Technical Rationale

When an AI system retrieves web documents, discoverable and relevant sources have a plausible opportunity to influence the response. Examining the sources already selected can reveal patterns and gaps. Repeated measurement can also expose competitive changes. These mechanisms justify research and experimentation, but the final response remains subject to platform-specific retrieval, ranking, generation, safety, and presentation systems.

Limitations

• Prompt sampling: no prompt set represents every real user question. Generated prompts may reflect a tool’s assumptions, while customer-entered prompts may overrepresent internal language.
• Answer variability: identical prompts can produce different answers across runs, dates, accounts, regions, and interfaces. A single observation is weak evidence.
• Collection differences: an API response, automated collection, and a consumer interface may not behave identically.
• Metric interpretation: visibility scores and share of voice summarize a defined dataset; they are not measures of total market exposure.
• Citations and recommendations: citation does not necessarily mean endorsement, and a mention does not mean inclusion in the final recommendation.
• Favorability: automated presence counts may miss nuance, qualification, factual errors, or negative context.
• Attribution: changes after content or PR activity may also reflect model updates, source-index changes, competitor actions, or random variation.
• Commercial outcomes: visibility does not establish traffic, conversion, revenue, or customer preference. Those require separate analytics and controlled interpretation.

15. History and Development

Ahrefs was founded in 2010 and initially became known for web crawling, backlink intelligence, and Site Explorer. It subsequently expanded into keyword research, rank tracking, site auditing, content research, web analytics, and educational programs through Ahrefs Academy. The academy has historically used free courses to teach SEO concepts and demonstrate practical product workflows.

The emergence of ChatGPT, AI Overviews, Perplexity, and other answer interfaces created a new measurement problem: brands could appear in generated responses even when no conventional ranking report captured that exposure. Ahrefs responded through research into AI citations and referral behavior, educational content about AI search, and Brand Radar, introduced in 2025 as a way to investigate brand visibility across search and AI-answer datasets.

The AEO course belongs to this later phase of Ahrefs’ development. It translates the company’s search expertise into a curriculum covering answer-engine research, measurement, source analysis, and optimization planning. The course launch should not be confused with Ahrefs’ 2010 founding or with the separate launch of Brand Radar.

Its longer-term relevance will depend on continued updates. AI-search platforms change rapidly, and durable instruction must focus on retrieval, evidence, entities, useful content, credible third-party presence, and rigorous measurement rather than temporary interface features or supposed ranking tricks.

16. How Companies Like Ahrefs Leverage Independent Consultants through Umbrex

Umbrex has built a global community of more than 8,000 independent management consultants based in more than 50 countries, including alumni of McKinsey, Bain, BCG, and other top firms. Companies engage Umbrex when they need experienced consulting talent without necessarily requiring a full traditional consulting team. Umbrex consultants work across strategy, operations, organization, marketing, sales, finance, technology, ERP, analytics, and AI.

Representative projects relevant to a search software and training provider such as Ahrefs include:

• GEO product strategy: define priority customer problems, platform coverage, workflows, and a roadmap from monitoring to recommended action.
• Measurement framework: design defensible metrics for prompts, mentions, citations, favorability, referrals, and commercial outcomes.
• Pricing and packaging: test how AI-visibility data should be packaged across individual, agency, team, and enterprise plans.
• Customer segmentation: quantify needs among SEO teams, brand functions, communications groups, agencies, and multinational enterprises.
• Go-to-market design: align free education, product-led adoption, agency channels, enterprise sales, and customer success.
• Competitive intelligence: compare AI-visibility platforms, SEO suites, specialist tools, and managed-service substitutes.
• International expansion: prioritize languages and markets using customer demand, platform availability, data feasibility, and local competition.
• Operating model and governance: clarify ownership across product, data, research, academy, marketing, sales, legal, and AI-governance teams.

These examples describe ways a company in this category could use independent consultants; they do not indicate that Ahrefs or Ahrefs Academy has engaged Umbrex.