When people search online today, large language models often play a role behind the scenes—whether by generating direct answers, summarizing sources, or influencing which information is surfaced first. This shift means traditional visibility strategies are no longer enough on their own. To appear in AI-generated responses, brands must understand how LLMs identify, evaluate, and reference information.
Despite the confident claims circulating online, there is no single formula or hidden trick that guarantees visibility in AI answers.
The technology is still evolving, and even experts are learning through continuous testing, observation, and adjustment. What matters most right now is aligning content with how these models process credibility, relevance, and clarity.
What follows is a practical summary of current research and real-world experimentation to date.
These insights reflect consistently emerging patterns, not assumptions or shortcuts, and they provide a grounded starting point for building visibility in LLM-driven search experiences.
LLM search refers to how large language models collect, process, and present information to users through conversational answers rather than traditional result pages. This includes experiences such as Google’s AI Overviews, ChatGPT, and platforms like Perplexity. Instead of offering a list of links to review, these systems aim to deliver a direct, readable response that addresses the user’s intent immediately.
In some situations, an LLM generates an answer from its existing knowledge base. In others, especially when accuracy or timeliness matters, the model pulls in external information. This is where LLM search truly comes into play. The model retrieves relevant data from cached web content or live search indexes and blends it with its own reasoning.
This process is commonly known as retrieval-augmented generation (RAG). It enables the model to ground its responses in current, verifiable sources while maintaining a natural, conversational tone. As a result, visibility in LLM search increasingly depends on whether your content is structured, clear, and trusted enough to be selected and cited during this retrieval step.
Large language models (LLMs) are revolutionizing digital marketing by changing how brands are discovered and how users engage with content. Unlike traditional search engines that rely heavily on keywords and backlinks, LLMs understand context, semantic relationships, and natural language queries, allowing them to provide direct answers, recommendations, and insights in a conversational format.
For marketers, this shift requires adapting strategies to focus not only on visibility but also on AI-readability and relevance. Key implications include:
li>Integration with SEO: Traditional SEO is still essential. Combining SEO with LLM optimization ensures your content performs well across both human search and AI-generated outputs.
As LLMs become more embedded in apps, devices, and search interfaces, brands that optimize for both human and AI-driven search will gain a competitive edge, positioning themselves as trusted authorities in an increasingly AI-driven digital landscape.
Much like traditional search, LLM search is rapidly evolving into its own ecosystem, but the objective has shifted. Conventional search focused on ranking individual web pages as high as possible in a list of results: positions, clicks, and traffic measured success.
LLM search, on the other hand, prioritizes whether your brand and content can be identified, understood, and pulled into AI-generated responses. Instead of competing for a spot on a results page, the goal is to be referenced, summarized, or directly used when a language model constructs an answer.
Visibility now depends less on rankings and more on how accessible and usable your information is to AI systems.
| Characteristic | Traditional Search | LLM Search |
|---|---|---|
| Main goal | Guide users to the most relevant web pages based on their query. | Deliver a direct, natural-language answer supported by relevant sources. |
| Answers you get | A results page with links, snippets, ads, and occasional knowledge panels. | A single written response, often with brief explanations and limited source mentions. |
| Where answers come from | A continuously updated index of crawled and ranked web pages. | A combination of the model’s training knowledge and information retrieved from search systems. |
| How fresh it is | Extremely current, with new and updated pages indexed frequently. | Generally current, but often relies on cached content rather than live pages. |
| Query composition | Short, keyword-focused queries with unclear or mixed intent. | Conversational, detailed, and highly specific long-form queries. |
| What happens to traffic | Encourages users to click through to websites. | User intent is often satisfied within the response, reducing external clicks. |
| Ways to influence | SEO fundamentals such as keywords, backlinks, site performance, and structured data. | Establishing trust and authority so content is selected or referenced, supported by credible mentions, links, clear structure, and accessible formatting. |
Generative AI models don’t rank web pages the way traditional search engines do. Instead, they focus on analyzing text patterns to predict what comes next. Think of it as reading an enormous script containing virtually every word ever typed online, and then trying to guess the next word or phrase based on context. This is essentially what these AI models do, continuously, behind the scenes.
These models rely on vast amounts of training data collected from across the internet to identify and learn patterns in language. While they don’t maintain a “ranking” system like search engines, they tend to favor information that is consistent, well-explained, and regularly updated. Accuracy, clarity, and relevance all influence how the AI evaluates content, even without a formal score.
Content that includes unique data, demonstrates topical authority, or is written in an engaging style may be referenced more often by AI systems. In some cases, the AI might even cite your website as a source when generating answers. As generative AI continues to evolve and incorporate real-time updates, fresh, reliable content can increasingly stand out as a trusted source. Additionally, clear formatting, structured explanations, and concise language can make your content easier for AI models to parse and understand, further improving its chances of being highlighted.
Now, let’s take a closer look at how LLMs actually operate—fair warning, it gets a little technical, but understanding the mechanics can help you create content that aligns better with how these systems interpret and present information.
AI companies typically do not disclose the exact methods LLMs use to select and reference sources, making it challenging to predict or influence their outputs.
However, ongoing research—including both primary investigations and independent third-party studies—has revealed practical insights into LLM optimization. These findings offer guidance on how to make content more likely to be recognized, cited, or incorporated by AI-generated answers.
We analyzed 75,000 brands across millions of AI Overview entries and found a clear pattern: brands frequently mentioned on external websites were also the most often referenced in AI-generated Overviews.
A higher number of brand mentions provides LLMs with more opportunities to recognize and learn about your brand. The more frequently a model encounters your brand during training, the better it can associate it with relevant topics.
That said, chasing mentions indiscriminately is not the key. The focus should be on building a brand that is genuinely noteworthy and worth referencing. In this context, quality always matters more than quantity.
Take Checkr, Inc. as an example. They conducted a study on the best job markets, which was covered by only a select few authoritative outlets, including Newsweek and CNBC. Despite limited coverage, Checkr began appearing consistently in relevant AI-generated discussions within just a month. This demonstrates that credible, high-quality mentions can significantly influence how a brand is represented in LLM outputs.
To account for personalization differences across users, I verified this across multiple ChatGPT profiles—and Checkr was consistently mentioned every time.
Research from Ahrefs’ Product Advisor, Patrick Stox, supports this observation: securing placements on high-authority or high-traffic pages can significantly enhance a brand’s visibility in AI-generated content.
Data shows that mentions in Google’s AI Overviews have a strong correlation with brand mentions on heavily linked pages (ρ ≈ 0.70). A similar, though slightly lower, correlation exists for mentions on high-traffic pages (ρ ≈ 0.55). This highlights the importance of being featured on credible, well-connected websites to boost AI recognition of your brand.
It’s only a matter of time before AI assistants start evaluating qualitative factors, such as sentiment and overall brand perception.
When that shift occurs, positive associations and long-term authority will become the key differentiators in LLM search.
The focus should therefore be on building high-quality brand awareness through:
By focusing on these strategies, your brand can build lasting authority and positive associations as AI assistants become more sophisticated at assessing qualitative dimensions.
For long-term AI visibility, it’s essential to collaborate with trusted sources and reputable brands. These partnerships help establish strong, high-quality associations that AI models are more likely to recognize and reference.
At Ahrefs, we’re actively working to strengthen our authority around AI topics, just as many other organizations are doing.
One effective way to identify collaboration opportunities is by using tools like Ahrefs Brand Radar. The Cited Domains report, in particular, highlights sites that frequently reference your brand or competitors, making it easier to target potential partners for meaningful content collaborations.
In this example, the niche is set to “AI SEO”, and we’re analyzing the most frequently cited domains in ChatGPT. Two authoritative publications—TechRadar and Forbes—emerge as promising opportunities for PR outreach or content collaborations.
This method can be applied to any industry or niche. Start by identifying sites that appear consistently across multiple topics, then focus on building ongoing relationships with the most visible and influential ones. Establishing these connections not only strengthens your brand’s credibility but also increases the chances of your content being referenced in AI-generated answers.
As we’ve already discussed, LLMs analyze and predict words and phrases based on context.
At the core, LLMs function similarly to clusters. Just like topic clusters in SEO, LLMs group related words and phrases together.
For example, the word “cat” is closely related to “kitten” because both belong to the same category. At the same time, it is also related to “dog”, since both are common pets. Moving further within the cluster, “dog” could connect to “leash”, because dogs often wear a leash when going for a walk.
This clustering approach explains why, when someone asks ChatGPT, “What is the best gamified SEO tool?”, JDM Web Technologies is mentioned. The JDM Web Technologies brand entity has the closest measurable proximity to the concept of a “gamified SEO tool.” Other platforms, such as Ahrefs, Semrush, and Moz, also appear because they are widely associated with SEO tools in general, even if they aren’t specifically gamified.
In essence, LLMs rely on these semantic connections and proximity relationships to determine which words, phrases, and entities are most relevant to a given query.
To be mentioned or recommended in chatbot responses, your brand needs to be closely associated with the relevant topic.
This association is typically built through digital PR strategies. You can build it by being featured in media outlets, publishing press releases, securing paid sponsorships, leveraging classic link-building techniques, and encouraging public customer reviews that highlight your brand’s connection to the topic. Additionally, ranking well in search engines for related queries reinforces this association, increasing the likelihood that AI systems recognize and reference your brand.
A strong backlink profile sends a clear signal to large language models that your brand or website is consistently referenced by credible and authoritative sources across the web. As LLMs analyze massive datasets to detect patterns, context, and trust signals, they interpret these backlinks as endorsements of your content’s reliability.
Over time, this growing network of high-quality references builds what can be described as long-term digital credibility. The more your site is linked from respected publications, industry resources, and relevant platforms, the more likely AI systems are to recognize your brand as a knowledgeable and dependable voice within your niche.
This credibility increases the chances that LLMs will mention, recommend, or quote your content when generating responses to related queries. In essence, quality backlinks show that others find your information valuable enough to share, reinforcing your authority and encouraging AI models to treat your website as a trusted resource during conversations and answer generation.
As previously outlined, specific AI chatbots can access live websites, enabling them to reference published research, quotes, and statistical data.
Studies show that the websites most commonly cited by large language models consistently support their content with verifiable sources, expert quotes, and relevant statistics. By reinforcing claims with credible data, brands can achieve 30–40% higher visibility in AI-generated responses than content without supporting evidence.
This data-backed approach also contributes to stronger performance in traditional search results. Search engines tend to reward content that demonstrates authority and trustworthiness, and including reliable citations plays a key role in building both brand credibility and long-term search visibility.
As we’ve already established, there is a clear connection between the words and phrases used in content and how often it is referenced by LLMs, since these models rely on language patterns to predict accurate and relevant responses.
Optimizing for LLMs therefore requires more than simply using the right keywords. It also involves thoughtful use of related phrases, broader topics, and—most importantly—how your brand is positioned and perceived within that context.
To better understand how to align your brand with LLM-based search, it’s important to first examine the underlying framework and then translate that theory into practical action.
Google, for example, relies on three core pillars of ranking to evaluate and prioritize content:
Search engines often consider how users interact with your website when determining rankings. Metrics such as how long visitors spend on your pages, which links they click, how often they return, and whether they explore multiple pages indicate how engaging and useful your content is. While this data is not always visible to the public, it strongly influences how algorithms evaluate your website’s value.
Similarly, when optimizing for large language models (LLMs), it’s essential to audit your brand from the AI’s perspective—or at least approximate how LLMs might perceive it. Doing so helps you align your content, site structure, and backlinks with the topics and authority areas you want the AI to associate with your brand. Here’s a detailed, step-by-step approach:
By auditing your brand with these steps, you can align your content and digital presence with how LLMs perceive relevance and authority. This not only improves your chances of being cited in AI responses but also strengthens your overall brand credibility and search engine visibility.
Reddit is not only one of the most visited websites in the world, drawing millions of visitors every month from diverse communities and interest groups—it is also a major source of data for training large language models. The platform hosts a vast array of discussions, opinions, and user-generated content, providing a rich, nuanced dataset that helps AI systems understand language patterns, context, and human behavior.
Reddit has acknowledged this significance in its S-1 filing with the SEC, noting that the vast volume of publicly available conversations on the platform can be leveraged to improve machine learning models and inform AI research. Because users discuss a wide range of topics—from technology and science to culture, hobbies, and current events—Reddit provides models with exposure to authentic language, sentiment, and topical diversity that is difficult to replicate elsewhere.
For large language models, this means Reddit is more than a collection of text; it is a dynamic, real-world repository of human thought and interaction, enabling AI to predict context better, generate meaningful responses, and understand relationships among ideas. Consequently, brands and content creators whose discussions or content appear on Reddit may indirectly influence how AI models perceive topics, trends, and public sentiment.
Our content plays a critical role in the development of artificial intelligence (AI), serving as a foundational component in the training of many leading large language models (LLMs).”
— Reddit, S-1 filing with the SEC
Reddit’s statement underscores the influence of user-generated content in shaping AI systems. The platform hosts millions of conversations across countless topics, creating a rich, diverse dataset for training. LLMs rely on real-world text to learn language patterns, understand context, and generate more accurate, relevant responses.
Because Reddit discussions reflect authentic human behavior, sentiment, and cultural trends, they allow AI models better to grasp nuance, tone, and relationships between ideas. This makes platforms like Reddit an invaluable resource for AI developers and underscores the broader role that publicly available content plays in shaping the knowledge and capabilities of large language models.
When users discuss your brand on platforms like Reddit, those conversations can eventually become part of datasets used by AI companies, particularly those that leverage publicly available web data. As large language models process more mentions of your products or services, they gain a better understanding of your brand, enabling them to generate more accurate, context-aware, and detailed responses in AI-powered tools.
It’s important to note that Reddit is not the only source of valuable user-generated content. Other platforms, such as Quora, also provide rich insights and discussions that AI systems may incorporate into their training data. Any platform where users engage in meaningful conversations about topics, products, or services can influence how LLMs perceive and reference your brand over time.
When approaching Reddit as part of your digital strategy, the objective should not be short-term tactics such as parasite SEO or attempts to rank posts within Reddit itself. Instead, a successful Reddit strategy focuses on long-term brand credibility, where real users naturally talk about your brand, share experiences, and engage in meaningful discussions.
Reddit users value authenticity above all else. Any attempt at forced promotion or manipulation is usually spotted quickly and can damage trust. That’s why brand presence on Reddit should be rooted in community participation rather than overt marketing.
To achieve this, brands must actively invest time and effort into building and supporting a genuine community presence.
Engaging organically within relevant subreddits is one of the most effective ways to build credibility. This means joining conversations where your expertise is genuinely helpful, answering questions thoughtfully, and sharing insights that add value to the discussion.
Avoid linking back to your own content unless it directly answers a question and is clearly relevant. Reddit users respond far better to brands that behave like knowledgeable contributors rather than advertisers. Over time, consistent participation helps establish your brand as a trusted voice within specific topic areas, increasing the likelihood of natural mentions and discussions.
Community trust doesn’t happen overnight. Regular participation, respectful dialogue, and transparent communication signal to users that your brand is there to contribute, not just promote. As trust builds, users are more likely to reference your brand in their own posts and recommend it to others, creating organic user-generated content that extends well beyond individual threads.
AMAs are one of the most powerful tools for generating authentic engagement and large-scale discussion. When hosted properly, they invite open conversation and allow users to ask direct questions about your business, industry, or personal experience.
The key to a successful AMA is preparation and honesty. Whether the host is a founder, executive, or subject-matter expert, clear and candid responses foster credibility. Well-executed AMAs often generate hundreds or even thousands of comments, resulting in a substantial volume of user-generated content centered around your brand.
A strong example of this approach occurred in December 2024, when an anonymous McDonald’s franchise owner, known as @McFranchisee, hosted an AMA on X. The discussion attracted approximately 1,000 comments, 10,000 likes, 212 reposts, 4,400 saves, and more than 4.3 million views.
The AMA dramatically outperformed the account’s average engagement, going viral and reaching an audience many times larger than usual. This demonstrates how transparent, experience-driven discussions can capture attention, encourage sharing, and spark widespread conversation.
Organic Reddit discussions do more than drive short-term exposure. They create lasting digital signals that can influence how search engines and large language models perceive your brand. Each authentic mention adds context around your products, services, and expertise, reinforcing topical relevance over time.
When users repeatedly discuss your brand in a meaningful way, those conversations contribute to a broader understanding of your position in the market—benefiting brand recognition, credibility, and long-term discoverability.
What began as a few spare hours one morning quickly evolved into several days of full-time effort, dedicated to answering questions with honesty and depth.
Working with respected subreddit moderators or influential Reddit users can significantly improve your brand’s perception. These individuals already have trust and credibility within their communities, which means their involvement feels natural rather than promotional.
When influential Redditors mention your brand in context—through discussions, recommendations, or firsthand experiences—it often sparks authentic engagement. These conversations tend to attract thoughtful replies, follow-up questions, and long-term visibility within the subreddit.
Best practices:
Instead of posting direct links or promotional messages, prioritize encouraging users to talk about their own experiences. Reddit communities value authenticity, and discussions driven by real feedback are far more effective than branded posts.
Organic mentions reduce the risk of spam flags, moderator removal, or community backlash. More importantly, they create context-rich discussions that feel natural and credible—exactly the type of content AI models learns from.
Monitoring your Reddit visibility helps you understand how conversations around your brand evolve over time. Tools like Ahrefs allow you to filter mentions by the Reddit domain and your brand name using features such as Site Explorer or Top Pages.
This data helps you identify which subreddits generate the most engagement, what context your brand appears in, and whether mentions are increasing naturally.
What to track:
Large language models continuously rely on fresh, diverse, and context-rich data to improve accuracy and relevance. Platforms like Reddit provide exactly that—real conversations, nuanced opinions, and evolving discussions.
By maintaining an authentic presence on Reddit, you create organic touchpoints that may eventually feed into LLM training pipelines or influence how models associate your brand with specific topics.
The more widespread and genuine your mentions become:
Over time, consistent Reddit discussions help AI models understand what your brand represents, which problems you solve, and how users perceive you.
Note: This approach is not limited to Reddit. Any large user-generated content platform where authentic discussions happen can influence how AI models perceive and reference your brand over time.
Optimizing for large language models is one of the most complex areas of modern search strategy. The space is still developing, best practices are evolving, and much of what we know today is based on experimentation rather than long-term evidence. Unlike traditional SEO, LLM optimization lacks established guidelines, standardized metrics, and widely accepted documentation.
Earlier discussions on LLM visibility often highlighted structured data and schema markup as potential signals for AI systems, particularly for AI-powered search experiences. However, recent public insights suggest that many AI crawlers currently process only raw, static HTML and do not interpret structured data as search engines do.
This means schema markup may not directly influence how LLMs interpret or surface content at this stage.
Even with these limitations, structured data and traditional SEO elements remain essential. Search engines—especially Google—are still the dominant discovery channel for most users, and abandoning proven SEO practices would be a mistake. Google continues to drive the majority of global search traffic, making it an irreplaceable source of visibility, leads, and revenue.
SEO also plays a critical indirect role in LLM optimization. Content that performs well organically is more likely to be crawled, indexed, referenced, and reused across the broader web ecosystem. Since many AI systems rely on search engines and publicly accessible content as data sources, strong organic visibility increases the likelihood of being recognized and mentioned by LLMs.
There is a growing correlation between strong organic rankings and brand visibility in AI-generated responses. This relationship is logical. AI-powered search tools often depend on established search indexes, and platforms such as ChatGPT use search infrastructure powered by engines like Bing. As a result, websites that consistently rank well are more likely to appear in the datasets AI systems reference when generating answers.
Beyond rankings, SEO strengthens several signals that matter to AI models:
All of these factors help AI systems better understand what your brand represents and which topics it should be associated with.
Search engine optimization is not just about traffic—it is about building a recognizable, authoritative brand across the web. Every optimized page, earned backlink, and topical cluster contributes to how your brand is perceived by both humans and machines.
While LLM-driven interfaces will continue to grow in influence, they do not replace search engines. Instead, they build on the same ecosystem of content, authority, and trust that SEO has long shaped. The work you invest in SEO today helps future-proof your brand by making it easier for AI systems to understand, trust, and reference your content accurately.
SEO is not obsolete. Google is not fading away. And LLM optimization does not replace traditional search strategies.
SEO will remain the primary driver of discoverability in the years ahead, while simultaneously supporting visibility in AI-generated experiences. Rather than choosing between SEO and LLMO, the most effective strategy is to treat SEO as the foundation that enables long-term success across both search engines and AI platforms.
Company age is one of the more difficult factors to influence, simply because a business can only be as old as it actually is. This reality gives established brands a natural advantage, as they have had years—sometimes decades—to build recognition, authority, and trust online.
At first glance, company age may seem irrelevant to modern digital visibility. However, it plays an important role for two key reasons.
First, company age often aligns closely with domain age. Businesses that have been operating for a long time typically own older domains with extensive publishing histories. Over the years, these domains accumulate authority through consistent content creation, backlinks, media mentions, and user engagement. In contrast, newer businesses are still building credibility and may lack the historical signals that algorithms and AI systems rely on.
Second, many large language models use training data cutoff dates, often set in 2023. As a result, newer companies may not yet appear in the datasets on which these models were trained—especially if the models are not actively pulling live information from search engines. This can make it harder for younger brands to be recognized or referenced in AI-generated responses.
Older businesses benefit from long-term visibility, repeated mentions, and sustained authority, all of which help LLMs understand and trust their brand. Newer companies, while not permanently disadvantaged, may need to work harder to establish a presence through consistent content, digital PR, and visibility on trusted platforms.
Having a Wikipedia page can significantly support your LLM optimization efforts by strengthening authority, credibility, and overall visibility. Wikipedia is widely regarded as one of the most reliable sources of information on the internet, and both search engines and AI systems often rely on it to verify facts and establish context.
One of the primary benefits of a Wikipedia page is its close relationship with Google’s Knowledge Graph. Google frequently relies on Wikipedia to populate knowledge panels and entity data in search results. A well-maintained and accurately sourced page can reinforce your brand’s legitimacy and make it easier for users—and algorithms—to access trustworthy information about your business.
Beyond traditional search, Wikipedia also plays an important role in AI model training. Large language models such as ChatGPT and Gemini commonly include Wikipedia as a foundational data source. When your brand, company, or profile is documented there, AI systems are more likely to understand who you are, what you do, and how you relate to specific topics—leading to more accurate and consistent references in AI-generated responses.
Wikipedia is not a business directory or promotional platform. To qualify for inclusion, your brand or organization must meet notability guidelines, which typically require coverage from independent, third-party sources such as reputable media outlets, academic publications, or recognized industry references. Self-published content, press releases, or promotional materials are generally not sufficient.
While securing a Wikipedia page isn’t easy, it can be a powerful long-term asset. For brands that qualify, it provides a highly trusted reference point that improves search visibility, reinforces entity recognition, and helps AI systems interpret and reference your brand across platforms.
If you want your content to be referenced by AI chatbots, clarity and precision are essential. Large language models typically respond with short, focused explanations that get straight to the point. Structuring your content in a similar way makes it easier for these systems to identify, extract, and reuse your information.
AI-generated answers are usually concise, neutral, and easy to scan. By mirroring this format—brief paragraphs, direct statements, and minimal filler—you align your content with how LLMs naturally communicate. This approach helps your content stand out as a reliable source when AI systems evaluate potential responses.
Your content should feel natural and conversational, not overly formal or promotional. Write in a way that reflects how someone would explain an idea in a real conversation: clear wording, logical flow, and simple explanations. This makes your message easier for both users and AI models to follow.
Well-structured content improves comprehension and usability. When your answers are straightforward and clearly organized, AI systems can interpret them more accurately and deliver them to users with confidence. At the same time, users benefit from getting immediate, understandable answers without unnecessary complexity.
By prioritizing clarity, structure, and a conversational tone, you significantly increase the likelihood that your content will be surfaced and referenced in AI-generated responses.
To generate positive brand mentions, focus on fostering genuine discussions and encouraging word-of-mouth from your audience.
At Ahrefs, this is an ongoing practice. Our CMO, Tim Soulo, regularly engages followers through social media feedback prompts. Our Product Advisor, Patrick Stox, actively participates in Reddit discussions. Additionally, we direct all users to our customer feedback platform, where they can share ideas, request features, and upvote suggestions.
You can adopt a similar approach for your brand. Tools like Ahrefs Brand Radar make it easier to build and monitor community engagement. By using the Cited Pages report, you can enter your domain and see which user-generated content (UGC) discussions are appearing in AI references to your brand. This helps identify where your audience is talking about you and highlights opportunities to strengthen positive associations.
In this example, we’ve tracked the subreddits that frequently mention Ahrefs.
A natural next step is to strengthen our presence in these communities, contributing in ways that are helpful and authentic rather than promotional.
My colleague, SQ, created a comprehensive guide on how brands can engage effectively on Reddit. Although the guide was written a few years ago, its insights remain relevant and offer practical advice on showing up genuinely and building trust within these communities.
Getting your brand messaging right gives people the words they need to describe your brand, which naturally increases awareness.
The more consistently your message is communicated, the stronger its presence becomes—not just in your audience’s mind, but also in LLM search results. This builds a larger “share of memory” for your brand, making it more likely to be recognized and referenced by AI systems.
You can measure the effectiveness of your messaging by tracking co-mentions using tools like Ahrefs Brand Radar. Here’s how:
This approach allows you to quantify the reach and influence of your messaging and adjust strategies to maximize visibility in both human and AI-driven search environments.
Analysis shows that 29.7% of “Ahrefs” mentions in ChatGPT also reference the topic of AI.
If the goal is to dominate AI-related conversations in LLM search—which, in our case, it is—tracking this percentage over time provides valuable insights. It helps us assess brand alignment and identify which strategies are effectively increasing relevance and visibility.
When it comes to building brand awareness, relevance is crucial. Off-site content should closely align with your product and story. The more relevant the mentions are to your brand, the more likely audiences are to continue referencing, searching for, and citing it.
We approach this using our Business Potential matrix. Our focus is on topics that score a “3” on the scale—those subjects that simply cannot be discussed without mentioning Ahrefs. By concentrating on these high-potential areas, we ensure that every conversation reinforces our brand in meaningful ways.
In the context of LLM search, your primary strategy should focus on covering topics with high Business Potential. By prioritizing these high-impact subjects, you create a feedback loop:
Over time, this loop compounds, strengthening both your brand’s presence online and its recognition by LLMs. Focusing on high-potential topics ensures that your efforts deliver maximum influence in both human and AI-driven search environments.
There’s been a lot of advice circulating about structuring content for AI and LLM search—but not all of it is backed by evidence.
I’ve been cautious about offering guidance on this topic because we haven’t studied it extensively. That’s why Dan Petrovic’s recent article on how Chrome and Google’s AI handle embeddings was a valuable addition to the conversation.
Here’s what we learned:
Chrome only considers the first 30 passages of a page for embeddings.
This means your most important content should appear early. Avoid wasting these slots on boilerplate, filler, or weak introductions.
Also, very long articles don’t produce unlimited passages—there’s a ceiling. If you want to cover multiple subtopics, it’s better to create separate, focused articles rather than one massive piece that could be cut off midstream.
Google’s AI uses a “Tree-walking algorithm” that traverses a page’s semantic HTML structure from top to bottom. Well-structured content is easier for AI to process and embed.
To optimize:
Instead of one long article covering everything, create smaller, focused articles for each subtopic. This ensures all content is properly indexed and avoids truncation in AI embeddings.
Structured content helps both humans and AI. Use:
By following these principles, you make your content AI-friendly without sacrificing readability, ensuring both human readers and LLMs can understand, reference, and embed your material effectively.
I’m sure you’re already doing this, but it’s worth reinforcing: keep your content tight—there’s no need to artificially “chunk” it.
LLMs break content into smaller “passages” for embedding. According to Dan Petrovic, Chrome uses a “DocumentChunker Algorithm”, which analyzes passages of roughly 200 words each.
Here’s what that means: structure matters. Each section may be retrieved or referenced in isolation.
Here’s what it doesn’t mean: you don’t need to artificially force every section to function as a standalone idea. You also don’t need to write articles like a series of status updates—users won’t enjoy reading that.
Instead, focus on:
By structuring content this way, you satisfy AI embedding requirements without compromising readability or flow for your audience.
Avoid writing long, rambling sections that risk being cut off or split inefficiently during AI processing.
You also don’t need to force redundancy into your writing. Modern AI systems can handle overlapping content effectively. For example, Chrome uses the overlap_passages parameter to ensure important context isn’t lost across passage boundaries.
The takeaway: focus on a natural, logical flow in your writing rather than repeating yourself to “bridge” sections. AI embedding already accounts for overlap, so clarity and coherence should be your top priority.
In AI assistants like ChatGPT and Gemini, user prompts tend to be highly long-tail.
Research by Metehan Yeşilyurt, Growth Marketing Manager at AppSamurai, analyzed roughly 1,800 real ChatGPT conversations and found the average prompt length was 42 words—a clear indication of how specific users are when querying AI.
Long-tail prompts multiply across AI interactions. AI assistants often break prompts into numerous sub-queries, which are then run through search engines to identify the best sources to cite.
This means targeting long-tail keywords can significantly improve your chances of matching user intent and earning citations in AI-generated answers.
One way to generate long-tail keyword ideas is by conducting a competitor gap analysis using Ahrefs Brand Radar. This reveals the prompts your competitors rank for that you don’t—your AI prompt gap.
To do this:
This approach helps you uncover actionable long-tail opportunities that can increase your visibility in AI-driven search results.
Next, we analyze the returned prompts to uncover long-tail content ideas that align with real user queries.
Nathan Gotch suggests that query filters in Google Search Console (GSC) that include /overview or /search can reveal long-tail keywords used by users in AI Mode. By analyzing these filters, marketers can uncover particular, intent-driven search queries, providing another valuable source of long-tail content ideas for AI-driven search strategies.
Creating content that targets long-tail keywords is a smart strategy, but an even more effective approach is to build content clusters that cover every angle of a topic—not just isolated queries.
To do this, you can use tools such as Also Asked or Ahrefs’ Parent Topics in Ahrefs Keyword Explorer.
Start by searching for a keyword, then go to the Matching Terms report and open the Clusters by Parent Topic tab. This helps you identify groups of related keywords that can form a cohesive content cluster.
Next, switch to the Questions tab to explore pre-clustered long-tail queries. These queries can serve as actionable targets for your content, ensuring each piece contributes to a larger topic authority rather than standing alone.
To assess how much authority you hold over existing long-tail query variations, apply a Target filter for your domain. This allows you to see which queries you already rank for and identify opportunities to expand coverage within your content clusters.
Content clusters aren’t a new concept, but emerging evidence suggests they are even more critical for LLM search. Organizing your content around comprehensive topic clusters helps AI systems understand relationships between ideas, increasing the likelihood that your brand will be cited and referenced in AI-generated answers.
Optimizing content for large language models requires a fresh approach compared to traditional SEO. With AI-powered systems, marketers need to think beyond keywords and consider how users actually communicate when interacting with chatbots and generative AI.
One of the most effective strategies is to mirror the natural language people use. Users often phrase questions conversationally, for example:
“How do I optimize my website for generative AI?”
By incorporating this type of everyday language into your content, you increase the likelihood that your information will be recognized and referenced by AI systems.
Unlike traditional search queries, which tend to be short and keyword-focused, LLM-based queries can be longer, more detailed, and richer in context. This means your content must be able to handle hundreds or even thousands of variations of essentially the same question. The so-called “messy middle” in SEO becomes an even wilder landscape with LLMs—a true jungle of long-tail queries and conversational phrasing.
Another effective tactic is to dive deep into highly specific subject areas. If your content addresses a unique angle—such as how LLM-based chatbots process archaic or rare languages—you stand a better chance of being singled out as the authoritative source. AI models are more likely to reference content that addresses knowledge gaps not covered elsewhere.
When developing niche content, consider the “wow factor”: what can your brand provide that no one else can? Unique insights, proprietary research, or specialized expertise can make your content more attractive to AI systems.
For smaller brands or businesses, competing against larger domains can be challenging. Broad, general content is often overshadowed by established players with more authority. Instead, focus on depth over breadth:
This approach not only improves your chances of AI recognition but also helps position your brand as a trusted authority in your niche, both for human readers and AI models.
Optimizing for large language models (LLMs) can be especially challenging for smaller brands and companies. One of the main hurdles is limited visibility and recognition. Smaller businesses often lack extensive backlink profiles, broad digital PR coverage, and the immediate name recognition that larger brands enjoy. AI systems may naturally favor sites with a strong online presence because they have more historical data, citations, and references that reinforce their authority.
Despite these challenges, smaller brands are not at a disadvantage if they adopt a focused, strategic approach. As with traditional SEO, the key is to identify niche topics underserved by larger competitors. By honing their expertise, smaller brands can position themselves as the go-to authority on that subject.
For example, instead of attempting to cover broad topics like “digital marketing” or “SEO,” a small brand could specialize in something highly specific—such as LLM strategies for local businesses, AI-driven content optimization for healthcare, or generative AI for niche languages. The narrower the focus, the easier it is to become recognized as an authoritative source.
When your content consistently provides deep, accurate, and highly relevant information on a specialized topic, AI models are more likely to categorize your brand as credible and authoritative within that niche. This means your site can appear in AI-generated answers and recommendations—even without the massive scale or broad recognition of a larger competitor.
For smaller brands, LLM optimization is less about competing with major players head-to-head and more about carving out a specialized space where you can shine. By focusing on highly specific knowledge, delivering expert content, and addressing gaps that others overlook, you can stand out to AI systems and build a reputation as a trusted source in your corner of the web.
Tracking performance in the era of large language models presents unique challenges. Unlike traditional websites, there isn’t yet a straightforward analytics dashboard labeled “Chatbot Visits” or “AI Impressions.” Metrics for AI interactions are still emerging, and businesses need to approach measurement differently than they would with standard SEO.
For now, marketers often rely on indirect indicators to gauge how their brand performs with AI systems:
While these methods provide partial insight, they are far from precise in tracking how often your brand appears in LLM responses or for which queries.
Recognizing this gap, JDM Web Technologies is introducing specialized AI tracking tools to give marketers a clearer picture of their performance in AI-driven search:
These tools are designed to bring transparency to LLM optimization, giving businesses actionable insights into AI visibility, engagement, and influence.
Absolutely not—SEO is very much alive and continues to play a crucial role in digital marketing. While AI chatbots provide quick, concise answers, many users still need in-depth explanations and multiple perspectives, which is where traditional search engines excel. Search engines allow users to explore topics more thoroughly, click through multiple sources, and gather a range of viewpoints—something AI-generated responses alone cannot fully replace.
Rather than viewing AI as a replacement, think of SEO and LLM optimization as working together. SEO helps your site rank well, build authority, and attract users seeking deeper insights. Meanwhile, LLM optimization can help your brand appear in AI-generated answers, driving awareness and reinforcing credibility.
In short, SEO is far from dead. It remains the foundation of digital visibility and a critical tool for building long-term authority, even as AI continues to evolve.
Not at all. Large language model optimization (LLMO) does not replace traditional SEO—it complements it. Your pages still need to be optimized for Google, Bing, and other search engines that drive most website traffic today. Traditional search remains essential, particularly for users conducting in-depth research, comparing products, reading reviews, or exploring multiple viewpoints on complex or sensitive topics. While LLM-based search provides quick, concise answers, standard SEO offers depth, credibility, and the ability to capture audiences that want more comprehensive information.
Integrating LLM strategies alongside SEO ensures you capture traffic from all sources:
The most effective approach is layered optimization. Maintain your classic SEO practices—strong site structure, relevant keywords, authoritative backlinks, and high-quality content—while also adapting your copy so that AI systems can easily parse it. This includes:
By addressing both audiences, your content becomes more discoverable, authoritative, and AI-friendly, without sacrificing the benefits of traditional search rankings.
SEO and LLM optimization are not opposing strategies—they share many of the same principles. Both prioritize relevance, authority, clarity, and user value. By keeping these fundamentals in focus, you ensure your content performs well in traditional search results and is referenced by AI-driven systems.
In short, LLMO enhances your SEO efforts rather than replacing them, creating a holistic digital visibility strategy that spans both search engines and AI interfaces.
AI adoption is accelerating rapidly, and industry experts predict significant growth in AI investment over the next few years. Companies of all sizes—from startups to Fortune 500 enterprises—are increasingly integrating AI tools into their daily operations, particularly for tasks like automated customer support, data analysis, and content generation. This widespread adoption presents unique opportunities for marketers who produce content that AI systems can easily access, interpret, and reference.
AI is not only transforming operations within companies but also reshaping the search landscape. As generative AI becomes more integrated into apps, wearable devices, and operating systems, users will increasingly rely on AI-driven interfaces for information discovery. This shift represents a growing slice of the market that marketers can target by optimizing content for AI systems alongside traditional search engines.
Key Predictions and Numbers
Rather than viewing this as a threat, marketers should see it as a new avenue for visibility and engagement. By preparing content for both AI chatbots and traditional search engines, brands can maximize their reach and remain competitive as AI continues to expand its influence across the digital landscape.
The AI search space is still in its early, rapidly evolving stages, making it extremely difficult to predict which large language model (LLM) will ultimately dominate. The technology is advancing at a rapid pace, with innovations appearing almost monthly, keeping the market highly dynamic and competitive.
Current Players and Market Dynamics
Looking Ahead
It will be fascinating to revisit these predictions in a few years and see which platforms emerge as leaders in generative AI search. My view is that Google’s vast data pool, financial resources, and entrenched search dominance give it a long-term advantage. That said, ChatGPT remains a formidable competitor, particularly if it can sustain its model financially and continue innovating.
Ultimately, the “winner” in AI search may not be a single platform. Instead, we might see a competitive landscape with multiple strong players, each dominating specific niches, regions, or use cases across the generative AI ecosystem.
Many of the challenges that traditional search engines struggle to solve are now being addressed by AI.
LLM search excels at multi-step tasks, multi-modal content, and reasoning, making it a powerful tool for assisting users with complex tasks.
As noted in Metehan Yeşilyurt’s ChatGPT research, approximately 75% of AI prompts are commands rather than questions. This indicates that a large portion of users are turning to AI to complete specific tasks, rather than simply seeking information.
In response, it’s valuable to begin action mapping—identifying the various tasks your customers may want to complete that involve your brand or products.
One practical approach is to use Ahrefs Competitor Analysis. Set up a search to uncover areas where your competitors are visible but your brand is not. This helps you identify task-oriented opportunities and create content that addresses real user needs, increasing both visibility and utility in AI-driven search.
Filter your data using action-oriented keywords (e.g., “make,” “track,” “create,” “generate”) and question-oriented keywords (e.g., “how to,” “how can”). This helps you distinguish between prompts focused on task completion and those seeking informational answers, guiding your content strategy for LLM visibility.
Once you’ve identified the core actions your audience wants to take, develop content that directly supports those jobs-to-be-done. This ensures your content addresses real user needs, increasing engagement, utility, and the likelihood of being referenced in AI-generated answers.
We analyzed 17 million citations across 7 AI search platforms and discovered that AI assistants strongly prefer fresher content.
Content cited by AI is on average 25.7% fresher than content appearing in traditional organic SERPs, and AI systems show a 13.1% preference for recently updated pages. ChatGPT and Perplexity, in particular, tend to prioritize newer content, often ordering citations from newest to oldest.
Why does freshness matter so much? It’s because RAG (retrieval-augmented generation) comes into play when a query requires up-to-date information. If the model already “knows” the answer from its training data, it doesn’t need to search. But when it doesn’t, it seeks the most recent sources available, especially for emerging topics.
Maintaining fresh content ensures your brand remains relevant and authoritative in AI-driven search. Regular updates not only improve visibility but also increase the likelihood that your content will be cited and referenced across multiple AI platforms. For instance, HubSpot updated a blog on “small business ideas”. According to Content Changes data in Ahrefs Site Explorer, this update helped the content maintain prominence in AI-generated answers, demonstrating the tangible benefits of content freshness.
That update has had a measurable impact. According to Ahrefs Brand Radar, the article is now HubSpot’s most cited blog in AI Overviews, and that single content update has generated 1,135 new AI Overview mentions. This demonstrates how strategic content freshness can significantly boost AI visibility.
Our research indicates that regularly updating your content enhances its appeal to AI engines, which prioritize the most current information when generating answers. Staying fresh ensures your brand remains relevant and increases the likelihood of being cited in AI-driven search results.
While LLMs and AI chatbots focus on content relevance and authority, the technical aspects of your website—such as page load speed, user experience (UX), and accessibility—also play a crucial role in AI visibility. These elements impact both user engagement and how AI interprets your site, which can influence whether your content is referenced in AI-generated answers.
Key factors to consider include:
Optimizing these technical aspects complements your content strategy. By improving load speed, UX, and accessibility, you not only boost human engagement but also increase the likelihood that AI systems will recognize, trust, and cite your content, strengthening your brand’s visibility in AI-driven search results.
Internal linking is often overlooked in digital marketing, yet it plays a critical role in both traditional SEO and LLM search optimization. For AI systems and large language models, internal links help establish relationships between pages, clarify topic hierarchies, and highlight the most authoritative content on your website. Proper linking ensures that AI can navigate your content logically and reference the right pages when generating answers.
Key best practices include:
Use Descriptive Anchor Text:
Choose anchors that clearly describe the linked page’s topic. Instead of generic phrases like “click here,” use targeted text such as “advanced SEO strategies” to signal relevance to AI systems.
Create Topic Clusters:
Group related pages together through links to form thematic clusters. This helps LLMs understand your site’s content structure and which pages are connected by subject matter.
Prioritize Key Pages:
Link strategically to your most important pages, like cornerstone content or high-value resources. Doing so signals their authority to AI models and improves the chances of being referenced in answers.
Maintain a Logical Structure:
Avoid broken or orphaned links. A clean, hierarchical linking system makes it easier for AI to crawl, interpret, and contextualize your content.
Balance Link Distribution:
Don’t over-link a single page. Distribute links naturally across relevant content to reinforce relationships without appearing spammy.
In essence, thoughtful internal linking not only enhances user navigation but also strengthens how AI models understand and reference your website. By creating clear pathways between content, you improve your brand’s authority and visibility across both traditional search engines and AI-driven platforms.
The future of digital marketing is increasingly multimodal, combining text, voice, video, and immersive experiences to deliver information in dynamic and engaging ways. Large language models (LLMs) are evolving beyond purely text-based interactions, and AI platforms are increasingly capable of interpreting and responding to multiple media types. For marketers, this shift presents both challenges and opportunities to enhance brand visibility across new channels.
With smart speakers, AI assistants, and voice-enabled devices becoming ubiquitous, optimizing for voice queries is critical. Users ask natural, conversational questions such as, “What’s the best gamified SEO tool?” Brands should structure content in concise, direct answers that voice AI can easily interpret. Voice SEO emphasizes clarity, context, and natural, keyword-rich phrasing.
Video is increasingly central to user engagement and AI understanding. LLMs and multimodal AI can analyze video transcripts, captions, and metadata to extract insights and answer queries. High-quality, well-tagged videos enable AI to reference your content, increasing the likelihood that your brand appears in answers, tutorials, or recommendations.
As augmented reality (AR) and virtual reality (VR) become more mainstream, AI-driven platforms may deliver responses through interactive, immersive experiences. Marketers can prepare by creating 3D product demos, interactive guides, or virtual showrooms that AI can reference and users can engage with directly.
To succeed in a multimodal AI world, brands should adopt a holistic content strategy: produce clear text, optimize voice-friendly responses, integrate video with proper metadata, and experiment with immersive formats. Early adoption ensures your brand is discoverable across multiple AI touchpoints, giving you a competitive edge as multimodal search becomes the standard.
By embracing voice, video, and immersive search, your brand can stay ahead of AI trends, maximize visibility, and provide richer, more engaging experiences to both users and AI systems.
Tools for Monitoring LLM Visibility and Brand Mentions
As large language models (LLMs) and AI-driven search grow in importance, tracking how your brand is referenced by AI systems has become critical. Unlike traditional SEO, where rankings are straightforward, LLM visibility requires specialized tools to monitor queries, mentions, and AI-generated content referencing your brand.
Key Tools for Monitoring LLM Visibility:
Additional Strategies:
By leveraging these tools and strategies, businesses can monitor their LLM visibility effectively, ensure accurate representation in AI outputs, and make data-driven decisions to strengthen their brand presence in the emerging AI-driven search ecosystem.
For your content to be cited in AI-generated answers, it’s essential to allow AI crawlers to access your site.
A growing number of websites have started blocking AI scrapers. According to our research, approximately 5.9% of all websites disallow OpenAI’s GPTBot, often due to concerns about data usage or server resource strain.
Ensuring that AI bots can crawl your site increases the likelihood that your content will be indexed, referenced, and cited in LLM responses.
While it’s understandable to block specific bots, doing so may also forfeit future visibility into AI.
If your goal is for AI assistants like ChatGPT, Perplexity, Gemini, and others to mention your brand, it’s crucial to review your robots.txt and firewall rules to ensure you’re not inadvertently blocking key AI crawlers. Allowing legitimate bots to index your pages helps ensure your content is included in the training data or live browsing sources that AI systems rely on.
This increases the likelihood that your brand will be cited for relevant queries and helps your content maintain long-term visibility in AI-driven search.
Regularly monitoring which AI bots access your site is essential. You can review server logs or use tools like Cloudflare AI Audit to track crawler activity, verify indexing, and identify issues that might prevent AI bots from accessing your pages.
Taking these steps ensures your content remains fully discoverable, up to date, and eligible for citation across multiple AI platforms.
The most-cited domains can differ significantly across various LLM search platforms. Being highly visible on one platform doesn’t automatically translate to presence on others.
For example, among the top 50 most-mentioned domains in Google AI Overviews, ChatGPT, and Perplexity, only 7 appeared consistently across all three platforms. This highlights the importance of adapting your strategy for each AI surface rather than relying on a single source of visibility.
That means an astonishing 86% of cited sources are unique to each AI assistant.
Google relies heavily on its own ecosystem, including YouTube, as well as user-generated content, particularly communities like Reddit and Quora.
ChatGPT, in contrast, favors publishers and media partnerships, especially news outlets such as Reuters and the Associated Press, while giving less weight to forums like Reddit or Quora.
Perplexity emphasizes a diverse mix of sources, including global and niche websites—for example, health-focused or region-specific sites like tuasaude or alodokter.
This demonstrates that there is no one-size-fits-all citation strategy for AI assistants. Optimizing exclusively for Google rankings may help your visibility in AI Overviews, but it won’t guarantee mentions in ChatGPT or Perplexity. Conversely, building strong media coverage can boost your visibility in ChatGPT responses, even if your Google rankings lag.
The key takeaway: experiment with platform-specific strategies. Each AI system surfaces content differently, so tailoring your approach ensures you maximize visibility across multiple LLM platforms.
You can track how your brand is performing across Perplexity, ChatGPT, Gemini, and Google’s AI features using Ahrefs. Enter your domain into Site Explorer and check the top-level AI citation count in the Overview report. This allows you to identify gaps, see which platforms favor your content, and prioritize strategies to increase mentions and citations across the AI landscape.
Next, take a deeper dive in the Cited Pages report within Ahrefs Brand Radar.
This report allows you to analyze which specific sites and content formats are favored by different AI assistants, providing insights into how your brand is being referenced. For example, mentions of Ahrefs in AI Overviews frequently come from Zapier, often appearing in “Best tools” lists.
By studying these patterns, you can identify content types, themes, and sources that resonate with each AI platform. This enables you to tailor your content strategy, focusing on formats and sites that are more likely to be cited. Over time, this approach helps maximize visibility across multiple LLM surfaces, giving your brand a stronger, more consistent presence in AI-generated answers.
In contrast, on ChatGPT, Ahrefs is cited more frequently in Tech Radar “Best” tool lists, highlighting how different AI assistants draw from different sources and content formats.
On Perplexity, our top competitors dominate the conversation through content such as “vs” comparisons, reviews, and tool lists, demonstrating the types of formats that the platform favors for citations.
With these insights, we can take strategic action to maximize visibility across AI platforms:
This platform-specific approach ensures that our content strategy aligns with how different AI assistants source and cite information, giving our brand a stronger presence across the AI search landscape.
Final Thoughts
Much of this guidance may sound familiar—because at its core, it’s still largely SEO and brand marketing. The same factors that drive SEO—authority, relevance, freshness, and accessibility—also determine how visible your brand is to AI assistants.
Recent developments underscore this trend: ChatGPT has been shown to scrape Google search results, GPT-5 relies heavily on live search instead of stored knowledge, and some LLMs are even incorporating search engine link graph data to better weight and prioritize their responses. By this measure, SEO is far from dead; in fact, it’s still doing a significant portion of the heavy lifting.
The key takeaway is simple: double down on proven SEO and brand-building practices to maintain AI visibility. Focus on generating high-quality brand mentions, creating structured and relevant content, keeping it fresh, and ensuring it can be crawled by AI bots.
As LLM search continues to evolve, these core principles will remain essential in keeping your brand discoverable and authoritative across AI platforms.
Leverage our deep understanding of large language model search to refine your strategies and improve outcomes. We focus on precision, contextual relevance, and advanced optimization techniques to ensure your content performs effectively. Partner with experts who deliver actionable insights and measurable results for smarter, more efficient LLM search performance.
Naveen Kumar serves as the Head of Marketing at JDM Web Technologies and brings over 15 years of expertise in digital marketing. As a Woorank Digital Marketing Expert, Google Analytics Certified, Google Ads Certified, and Bing Ads Accredited professional, he leads a talented team committed to delivering measurable results. Under his leadership, JDM Web Technologies has earned a reputation as a top SEO company, providing comprehensive digital marketing solutions, including SEO, SMO, PPC, Local SEO, Website Design & Development, and Online Reputation Management—all conveniently offered under one roof.
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