Full SEO / LLMO Workflow: Build Topical Authority for Any Domain

Using InfraNodus and its SEO-based companion KeywordGraph, you can build topical authority in any domain to ensure that your content and products get to the top of search results and LLM output. 

We ourselves, as well as our certified partners, have successfully applied this approach to our own content and also multiple other projects we're working on. We present this methodology below and demonstrate how it can be implemented using both the visual graph interface and / or the MCP server (directly inside an LLM).

The methodology is based on understanding how LLMs and search engines see a certain domain using a knowledge graph representation. This helps us see what's structurally important and which topical clusters should be covered in order for content to rank higher in search results for the main search queries in this domain. This same graph may also reveal content gaps between topical clusters which could be addressed to increase the so-called informational gain metrics used by Google.

A further layer — and one that's become essential for LLM optimization in particular — is to understand where your brand, methodology, and target terms currently sit within the co-occurrence structure of the wider conversation. LLMs learn associations from which terms appear near which other terms across the web, so being densely co-occurring with the vocabulary of your target clusters is what makes you retrievable. The same knowledge graph approach reveals where you're already embedded, where you're absent, and where seeding your terms would most efficiently shift your position.

The next step is to estimate search intent for that same set of search queries in order to understand what people are actually searching for. It may turn out that there are some topics that people search that are not readily available in LLM output or search results, which may indicate untapped content gaps and opportunities.

Finally, we combine these insights together to generate a content strategy that incorporates both the AI / top search results topical structure as well as the search intent and content gaps identified. This strategy can then be converted into an article, a website section (based on the pillar - spokes structure), or a whole website.

Each of the steps above can be enhanced with multiple improvements, so we explain them in detail below.

Step 1: Develop a Topical Structure for Your Content

In this step, we are developing a topical structure for your content to ensure the highest authority. In order to do that, we analyze LLM output for queries related to your domain as well as the top Google search results. This helps us understand what both LLMs and search engines consider to be important in the context we want to improve our rankings in.

1/1: Understand How LLM Sees Your Domain

If you want to get into LLM output for a certain subset of queries related to a certain domain, your content or product needs to have authority in that domain. 

This authority can be built in two ways: through content and through references (backlinks). While backlinks are not as important for LLM outputs, AI agents use search tools and sometimes Google or Bing to retrieve the results, so they still play an important role in SEO / LLMO. 

Let's consider you decide to go for both to maximize the results. Then you need to understand what structure your content (and backlinks) should have in order for LLM to think that your resource is, indeed, authoritative.

One of the simplest approaches is to ask the model what it considers important when discussing a specific domain. By creating content that not only addresses these topics but also connects them in innovative ways and introduces related, original ideas, the model will likely view your resource as high-quality.

For example, let's say we want to study the domain of topical authority. We can use the AI ontologies import app from KeywordGraph and make a request for "topical authority", we will then generate a knowledge graph for this topic that will enable us to understand the topical structure of how an LLM model (Claude 4.6 in this case) sees this domain:

The main topical clusters in this knowledge graph (for the topic "topical authority") are:

• Authority Metrics (google metrics, domain authority, topical authority)
• Link Structure (content hubs, internal linking)
• SEO Dynamics (meta tags, backlinks)
• Content Alignment (content strategy, semantic relevance)

This means that if we create content that's supposed to be authoritative for "topical clusters" domain, we need to make sure we talk about all these topics and concepts.

Both InfraNodus and KeywordGraph can also generate a topical outline — summarizing each cluster using AI — this additional information can be used to add more information about each cluster:

It's even better if we organize our content structure (and links between the pages) following this topical structure. For instance, if the main landing is the "Topical Authority" page / section, it can have links to the hub pages that each represents the topical cluster we identified and has spokes to more detailed pages that talk about various aspects of this subtopical cluster.

There are also latent topical clusters, such as:

• Coverage Gaps
• Traffic Goals
• Engagement Quality

We can use these to create more subsections and even emphasize them because they are usually new, trending, or underpresented topic, which means that an LLM might ingest the content that elaborates on these clusters because it might have less information on them in its own training data.

This approach becomes even more powerful when you use internal linking or backlink structure ensuring that the links themselves appear in the context that relates to the specific domain you want to rank in. The easiest way to do that is to create links between the different hub / spoke pages to replicate the graph structure. A more costly approach is to engage link building that would strategically position each cluster of your hub / spoke pages in relevant contexts at external websites with higher authority.

At this point, it can be sufficient to create a preliminary content structure based on the objective without yet writing the actual content, because we need to augment it with the analysis of existing search results.

We can copy the topic names as well as their descriptions to the Project Notes and use them later for content creation.

Tool to use:

InfraNodus interface: AI Knowledge Graph Generator (ontology-based)

Keyword Graph: How AI Sees a Topic (text-based)

MCP server: generate_ontology tool or analyze_llm_results tool for generating the graph with clusters and gaps, generate_topical_clusters for generating topical clusters with topical summaries

Workflow:

1. Generate an AI graph for a topic
2. Copy the topical clusters identified to the Project Notes (or add clusters and concepts to the context when using the MCP server)
3. Generate Topical Outline Summary with AI module and save it to Project Notes (or run the generate_topical_clusters MCP tool and add them to the context)
4. Optionally: save the content gaps and main relations, as well as latent ideas and top concepts to generate content that will bridge these gaps while taking the relations into account

Outcome:

You will have a list of topical clusters and summaries for each topic that represent how AI sees a certain domain. You can use this as a part of your prompt to generate content that covers all of these topics.

At this point, it's not necessary to generate content yet, it is sufficient to have a list of topical clusters and their summaries in order to generate content later.

1/2: Entity Analysis: Building an LLM Ontology Graph

For more in-depth analysis, you can also build an entity graph using the AI: Ontology import in InfraNodus / KeywordGraph or with the generate_ontology MCP tool in both.

This will produce a knowledge graph with LLM-generated ontology for your particular topic, which can be very helpful to understand not only what LLM thinks about your topic but also how it relates the different concepts together.

This is also a very useful view to understand a certain domain:

For instance, you can see here that the notion of "topical authority" is related to "content silos", "content farms" and "trustworthiness", which yield more nuanced understanding of what the content should focus on to gain authority in this field (and what should be avoided, as we're going slightly meta here with the example). 

1/3: SERP Analysis: What Content Ranks on Google

The next step is to understand the current informational demand: what content on in our domain ranks high on search engines. This will help us verify the topical clusters identified in Step 1 and ensure that we also include the clusters that search engines consider to be important to a particular query or domain we're exploring.

In this case, it usually makes sense to run this analysis on keywords, not entities, to let the model have richer context to extract topical clusters. We can use the Google search results analysis tool or KeywordGraph's SERP Analysis tool to get the results:

Using a similar approach as in step 1 we can save those clusters and generate AI summaries into our Project Notes. 

This graph will give us a good representation of the kind of content that ranks on Google for the "topical authority" query.

Interestingly, comparing to LLM output, 3 clusters stand out:

• Content Quality
• Entity Coverage
• Information Sources

These clusters do not really exist in the first LLM graph, so they may be particularly important for Google to assume that the page / website has authority on the subject.

We can also click each topic to study the actual search results in more detail to understand what they're about or ask the MCP server to add the actual statements to the context.

Tools to Use:

InfraNodus: Google Search Results analysis

Keyword Graph: Google SERP analysis

MCP server: analyze_google_search_results 

Workflow:

1. Generate a graph for a search query (queries) that relate to the domain where you want to rank
2. Save the keyword list, topical clusters list, as well as AI-generated topical summaries
3. Pay particular attention to content gaps and to what's present in Google search results but not in LLM output and vice versa — these may be potentially important topical clusters to target 

Outcome:

At this point, after completing steps 1 and 2, you will have a complete representation of what both LLMs and search engines consider to be authoritative content in your domain. The topics you should target and the gaps that can be found in both.

1/3: Find Content Gaps for Informational Gain

In this step, we use the graph of search results we generated earlier, but we focus on the gaps between topical clusters. This will indicate to us what ideas are present in current discourse (that ranks high by search engines and LLMs) but is are not yet connected. 

Targeting these content gaps will help us create content that will cover the blind spots in the current informational supply. The content will be relevant because it covers important topical clusters but also novel and important to search engines and LLMs because it will be bridging them in a new way. Google's algorithm take this novelty into account according to the informational gain patent (which is most likely implemented into their search algorithm), ranking the pages that contribute something new to the discourse (or something that the user hasn't seen yet) better than other results.

For our example, we identify the gap between SEO Strategy and Case Analysis clusters:

We then use the built-in AI to generate a research question (that we can use as a prompt) that will serve as a starting point for content creation. In our case, it's proposing to create a case study that would link "topical authority SEO strategy" cluster to "Case Analysis" cluster — showcasing how effective the topical authority approach is in practice. A great idea for novel content!

Tools to Use:

InfraNodus & Keyword Graph: Google Search Results analysis > Gaps > Gaps to Bridge > AI: Question Topical Gap

MCP server: generate_content_gaps

Workflow:

Visualize Google search results of LLM output and then open the Graph > Content Gaps panel to see the gaps. 

Reiterate through the gaps to find the ones that seem most relevant.

Use AI: Question Topical Gap to generate a prompt question to serve as a starting point for content that bridges the content gap.

Outcome:

You will have high-quality prompts and ideas that will expose the blind spots in current discourse and help you create highly original content to maximize informational gain for your audience, thus helping you rank higher in Google search results and get cited by LLMs.

Step 2: Map Co-Occurrence Position in the Wider Conversation

Another important aspect for LLMs and modern search engines is co-occurrence — which terms appear near which other terms, in which contexts, across which sources. Brand mentions are one important case of this, but the deeper principle is that any term you want to be retrievable for — a keyword, a concept, a methodology name, or your own brand — needs to co-occur, densely and across diverse sources, with the contextual vocabulary of the neighborhood you want to be retrieved from.

This is where InfraNodus and KeywordGraph are structurally suited to the task, because its graphs are built from word and entity co-occurrence — the same distributional principle that underlies how LLMs form associations between concepts. The SERP graph you build in Step 1 already shows the co-occurrence structure inside the top-ranking pages for your target queries. But that graph maps the competitive landscape, not your position within it, and it draws only from ranked search results — a filtered slice of the much wider corpus that LLMs actually retrieve from and train on.

So the next step is to build a second graph: the co-occurrence neighborhood your brand and target terms currently live in. 

You can do this by analyzing search results for your brand combined with category terms ("[your brand] [target topic]", "[your brand] vs [competitor]") using the InfraNodus Google Search Results tool or the KeywordGraph SERP analysis tool. 

It is usually enough to analyze the search snippets, but one can also feed in the URLs of articles, Reddit threads, podcast transcripts, and YouTube videos where the brand or methodology is discussed (there's a mode in Google search results tool of InfraNodus and SERP tool of KeywordGraph where you can analyze the actual pages).

This gives you a representative graph of the contexts where you're already mentioned across the web:

Comparing this brand-presence graph to the target SERP/intent graph reveals the seeding map: which topical clusters you're absent from but should be present in, which adjacent clusters bridge to the ones you want to own, and which distinctive vocabulary needs to circulate more widely to densify your associations. 

Each missing relation is a context where getting mentioned — through a guest post, podcast appearance, expert quote, original research, or contribution to community discussions — would shift your position toward the clusters that matter.

This is also why distinctive vocabulary matters. A specific phrase you coin and use consistently acts as a handle: every time it appears, it carries the associations you've built around it. Generic phrasing dissolves into the background distribution; distinctive phrasing creates retrievable structure.

For instance, in the example above, we can see that when we search for "infranodus topical authority", the results that we get show:

• Knowledge graph and Text insight clusters are prominent in how Google sees InfraNodus — a highly technical positioning for niche audience
• At the same time, Google's and AI's views of the topic itself (topical authority) is actually more focused on Authority Metrics, Content Quality, Entity Coverage

This means that we need to shift the positioning of the tool from purely technical representation to more business-oriented objects (increasing authority, various metrics, improving content quality, making sure all relevant entities are covered, etc)

Tools to Use:

Important: the request should include your brand / product name + the domain of study, because you're studying how your brand / product is currently embedded into the context.

InfraNodus: Google Search Results analysis

Keyword Graph: Google SERP analysis

MCP server: analyze_google_search_results and difference_between_texts (to analyze what's present in the graphs generated during Step 1 that's not present in this graph generated in Step 2)

Workflow:

1. Generate a graph of search results that maps co-occurrence of your brand / product with the domain where you want to promote it
2. Find the main topical clusters
3. Look at the differences

Outcome:

You will have an understanding of the topics you already rank good for and what topics you should emphasize when you talk about your product or service to rank high in search results.

Step 3: Estimate Search Intent

Now that we know what information exists out there and how our current content fits into the current informational supply, we should also study informational demand. 

If we know what people search for, we can ensure that our content caters to our audience's needs and use the language they use to find our product. 

Additionally, we may be able to identify the gaps between what people search for and what they actually find and target those with our content strategy.

For instance, for the "topical authority" query, we have this graph here. It shows the phrases that people who search for "topical authority" also search for (taken from Google's "people also search for" dropout menu and / or from Google Adwords suggestions):

It automatically removed the search terms from the graph ("topical" and "authority" — at the top right), so we can see the context around these queries.

The graph shows that 

•  "keyword research tools" 

is quite an important cluster. In fact, while "topical authority" is an important topic, it is adjacent to the more traditional "keyword research tools" query. So if we create content that would be interesting for the people who search for "topical authority", we also need to create content on the more traditional "keyword research tools" (comparisons, information, etc). 

This is a great insight that will modify our content strategy. While we identified in earlier stages that we need to talk more about "content quality", "entity coverage" and "metrics", this also shows us that we need to have a whole section on the more traditional "keyword research" tools and techniques, because it still has better search volume than these newer clusters.

Tools to Use:

InfraNodus: Google Search Intent analysis

Keyword Graph: Google search intent analysis

MCP server: analyze_google_search_intent and search_queries_vs_search_results or  difference_between_texts (to analyze what people search for — this Step 3 — but don't yet find — Step 2)

Workflow:

1. Generate a graph of search queries people use when they search for your topic (you can also use AHref or SemRush data export CSV files here)
2. Find the main topical clusters using the graphs
3. Identify what people search for but don't yet find using the comparison view (to search results / LLM output)

Outcome:

You will have an understanding of the gaps between the current informational supply and demand: what people search for but do not find so easily. Target those gaps to have content that caters to your audience's interests.

Step 4: Content Strategy Implementation

This is where it all comes together:

• In Step 1, you revealed patterns and gaps in the current informational supply via Google search results and LLM output analysis — the topical clusters you need to cover to maximize your topical authority.
• In Step 2, you analyzed the content of your own pages to understand how it fits in the current  supply and what topics you need to focus on to gain more authority in your realm.
• In Step 3, you analyzed current demand: finding patterns in typical search queries and LLM questions prompts people use to find your content. You found the outliers that people search for but do not easily find.

Now is the time to fuse the outcomes from each stage of the process and produce the content strategy that will take you to the top of the search results and LLM output for this particular topic. 

It is important to note that any successful LLMO / SEO strategy should also be strong on all the technical aspects: e.g. meta-tags, schemas, header titles, as well as the fast load pages (quick win: convert all images to .webp, add cache), canonical URLs and redirects (to avoid duplication), backlinks.

In this article, we focus on content only. Once we have a clear idea of the topics we need to cover, the gaps we need to address, and the keywords we need to use, we need to create the content structure that will work for this particular strategy. 

One of the most successful ways to implement is to start from the topical structure that we identified when analyzing the current informational supply using the knowledge graph. These topics can serve as the backbone of an article via the header tags (H1 to H4) if we're working on a single piece of content. Alternatively, the topical clusters can also define the so-called hubs - spokes structure, where every cluster becomes a pillar page (a hub) and the concepts and entities it's connected to are covered in adjacent articles (spokes). This is a very effective strategy to signal authority in any subject and provides a structure to your website that reflects how your realm is seen by search engines and LLMs.

In order to do that, you can use the AI functionality inside InfraNodus / KeywordGraph  — combine all the graphs together and then ask the AI module to generate a SEO Content Strategy Outline:

As you can see, when all the graphs are combiined, we get a pretty good content structure for hubs and spoke pages, which we can then implement using Claude Code or human writers.